Cuestionario Ampliado del Censo de Población y Vivienda 2020

El cuestionario ampliado se guarda en un un archivo .RData.

data <- read_sav("~/Personas_Censo 2020.SAV")
save(data, 
      file = paste0(here::here(), "/Bases/Censo_Personas_2020.RData"))

Se seleccionan las variables que se desean conservar para la realización de este documento y se guarda en un archivo .RData para practicidad del manejo de datos.

Posibles variables que se pueden contemplar en la migración reciente

  • EDAD
  • SEXO
  • AFRODES 
  • HLENGUA 
  • QDIALECT_INALI
  • PERTE_INDIGENA 
  • NIVACAD
  • ALFABET
  • CAUSA_MIG 
  • SITUA_CONYUGAL 
  • CONACT 
  • HIJOS_NAC_VIVOS

La variable mydata contiene 15 015 683 observaciones y 12 variables.

load(paste0(here::here(), "/Bases/Censo_Personas_2020.RData"))

mydata <- data %>%
           select(CVE_ENT, ENT, MUN, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES15, EDAD, 
                  SEXO, AFRODES, HLENGUA, QDIALECT_INALI, PERTE_INDIGENA, NIVACAD, ALFABET, 
                  CAUSA_MIG, SITUA_CONYUGAL, CONACT, HIJOS_NAC_VIVOS, FACTOR, ESTRATO, UPM)

save(mydata, file = paste0(here::here(), "/Bases/01_Migracion interna_2020.RData"))

✔️A partir de aquí se pueden correr los códidos 👇.
Se carga el archivo Migracion interna_2020.RData.

load(file = paste0(here::here(), "/Bases/01_Migracion interna_2020.RData"))

# Para fines prácticos se genera un ponderador de uno 
mydata <- mydata %>%
           select(CVE_ENT, ENT, MUN, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES15, EDAD, FACTOR, ESTRATO, UPM) %>%
            mutate(M = 1)  %>%
             mutate(NOM_ENT = as.factor(.$CVE_ENT)) %>%
              ungroup()

Claves de entidades y municipios

Se genera un vector con el nombre de las entidades llamado estados para facilitar los filtros en el documento.  Se genera un vector con las abreviaturas de las entidades llamado ent para fines prácticos. 
Se genera un vector con las claves de los municipios, pero es importante hacer notar que tres municipios no entraron el muestreo del Cuestionario Ampliado.

# Claves de los estados
estados <- sjlabelled::get_labels(mydata$CVE_ENT)

nom_estados <- c( "Aguascalientes", "Baja California" ,"Baja California Sur", "Campeche", "Coahuila de Zaragoza",
                  "Colima", "Chiapas", "Chihuahua", "Ciudad de México", "Durango", 
                  "Guanajuato", "Guerrero", "Hidalgo", "Jalisco",  "México", 
                  "Michoacán de Ocampo", "Morelos", "Nayarit", "Nuevo León", "Oaxaca", 
                  "Puebla", "Querétaro", "Quintana Roo", "San Luis Potosí", "Sinaloa", 
                  "Sonora", "Tabasco", "Tamaulipas", "Tlaxcala","Veracruz de Ignacio de la Llave", 
                  "Yucatán", "Zacatecas")

est <- c("AGS", "BC", "BCS", "CAMP", "COAH", "COL", "CHIS", "CHIH", "CDMX", "DGO", "GTO", "GRO", "HGO",
         "JAL", "MEX", "MICH", "MOR", "NAY", "NL", "OAX", "PUE", "QRO", "QROO", "SLP","SIN","SON", "TAB", 
         "TAMS", "TLX", "VER", "YUC", "ZAC")

# Claves de los municipios
MUN <- readRDS(paste0(here::here(), "/Bases/municipios_2020.RDS"))
nom_municipios <- sjlabelled::get_labels(MUN$NOM_MUN) %>% as.factor()
municipios <- sjlabelled::get_labels(MUN$CVE_MUN) %>% as.factor()

# Se le asignan las etiquetas a los nombres de los estados 
levels(mydata$NOM_ENT) <- estados

Población de 5 años y más

Se identifica a la población de 5 años y más.

filter(EDAD >= 5 & EDAD != 999)'.

Pob.5ymas <- mydata %>%
              as.data.frame() %>%
               mutate(EDAD = as.numeric(.$EDAD)) %>%
                subset(EDAD >= 5 & EDAD <= 130) %>%
                 filter(ENT_PAIS_RES_5A %in% estados)  # Filtro del lugar de residencia dentro del país. 

Nivel estatal

Migración reciente

Se utiliza la paquetería survey para poder trabajar con la muestra del cuestionario ampliado, en la cual se selecciona a la población de 5 años y más.

options(survey.lonely.psu = "adjust")

MC <- mydata %>%
       as.data.frame() %>%
        mutate(EDAD = as.numeric(.$EDAD)) %>%
         subset(EDAD >= 5 & EDAD <= 130) %>%
          filter(ENT_PAIS_RES_5A %in% estados) %>%
           svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)

saveRDS(MC, file = paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/MC_estado.RDS"))

Se genera una matriz cruzada del lugar de residencia hace 5 años a nivel estatal, utilizando la función svytable de la paquetería survey.

MC <- readRDS(paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/MC_estado.RDS")) 

Migrantes <- svytable(~ENT_PAIS_RES_5A + ENT, design = MC) 

La función cross_cases() de la paquetería expss se utiliza para crear tablas de contingencia cruzadas a partir de dos o más variables categóricas. Utilizando el comando weight, permite ponderar las observaciones “factores de expansión” en la tabla.

Se quita la diagonal a la matriz cruadrada con la función diag.remove() de la paquetería sna, donde esta función reemplaza los elementos de la diagonal principal de una matriz por un valor nulo o por el valor especifico.

Migrantes <- Migrantes %>%
              as.data.frame() %>%
               expss::cross_cases(ENT, ENT_PAIS_RES_5A, weight = Freq) %>%
                as.data.frame() %>%
                 slice(-33) %>% 
                  select(-row_labels) 

rownames(Migrantes)<- nom_estados
colnames(Migrantes) <- nom_estados

save(Migrantes, file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.RData"))

wb <- createWorkbook()
addWorksheet(wb, "MREst. 2015-2020")
writeData(wb, 1, Migrantes, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.xlsx"), overwrite = TRUE)

Matriz de migración reciente hace 5 años a nivel estatal, 2015 - 2020

Matriz de migración reciente 2015 - 2020
Nivel estatal
Entidad Aguascalientes Baja California Baja California Sur Campeche Coahuila de Zaragoza Colima Chiapas Chihuahua Ciudad de México Durango Guanajuato Guerrero Hidalgo Jalisco México Michoacán de Ocampo Morelos Nayarit Nuevo León Oaxaca Puebla Querétaro Quintana Roo San Luis Potosí Sinaloa Sonora Tabasco Tamaulipas Tlaxcala Veracruz de Ignacio de la Llave Yucatán Zacatecas
Aguascalientes 1226454 1113 180 0 771 556 430 2213 5837 524 4141 956 754 10468 6.3e+03 992 1219 897 1957 733 1356 1056 724 1247 958 182 360 1005 334 1251 160 11929
Baja California 583 3155213 6448 226 1621 3610 33627 3846 8079 2763 4217 26055 1958 13918 1.3e+04 14481 3180 7922 935 14018 6696 1608 754 886 38010 19619 2613 1099 624 14633 394 2209
Baja California Sur 139 4156 655361 120 521 485 3367 508 4574 515 725 16452 572 4143 5.1e+03 663 1063 1218 483 3123 3034 426 2038 183 7263 2064 523 294 638 3769 82 198
Campeche 55 196 150 795746 100 121 3024 156 1632 49 321 510 198 402 1.1e+03 368 96 31 821 982 719 136 4164 192 130 612 7793 1299 9 5853 4167 139
Coahuila de Zaragoza 220 780 276 304 2761903 50 3732 5757 3598 9722 2825 1339 691 2048 3.6e+03 969 702 121 8925 2181 2254 620 214 1879 865 1408 1573 3747 270 12106 284 3983
Colima 93 1343 189 112 146 633038 1686 471 1888 204 781 3038 281 13251 2.3e+03 5079 364 600 333 419 397 314 153 236 562 603 619 453 139 1074 80 166
Chiapas 127 4528 673 1767 438 286 4849943 1051 5449 200 855 1236 349 1578 4.9e+03 742 480 433 1348 5406 2618 364 5255 224 572 1058 5681 1457 108 5517 650 95
Chihuahua 1143 2315 580 1275 7154 208 4458 3287846 4614 14871 1763 1927 1116 4058 3.2e+03 1094 2278 565 2475 7943 3578 707 918 703 5361 2677 3106 680 18 24264 725 3171
Ciudad de México 2714 3878 1615 1696 1619 863 7091 2811 8274545 1129 4449 12498 10955 8594 1.6e+05 7117 9436 1205 6037 14199 21666 7695 6340 2329 2954 2312 3341 3047 2690 22919 3255 1025
Durango 627 2452 527 34 9038 127 220 7590 1482 1598892 1489 643 508 1641 1.4e+03 912 219 527 2239 493 544 524 152 419 3600 1368 177 865 221 1070 25 2516
Guanajuato 1804 2158 459 452 2137 1185 1299 1226 12928 985 5459604 2988 2155 8012 1.4e+04 10133 1615 667 2852 2457 2931 12161 1227 2103 1257 1190 1040 3041 794 5730 131 3157
Guerrero 91 2899 1875 226 385 575 1394 612 8523 282 864 3111059 883 3027 1.1e+04 5413 7787 525 680 3096 2884 724 1448 344 2307 849 353 496 133 1660 400 106
Hidalgo 405 1414 305 255 705 381 1089 730 35797 246 1453 1948 2665783 2627 7.0e+04 1543 1283 338 3492 1977 6914 3857 667 1732 798 1112 1375 2022 1844 9244 329 405
Jalisco 4421 8088 2883 191 2289 11300 7391 2785 15529 2387 10292 10691 5804 7375857 1.8e+04 19378 2360 14027 4431 5146 5181 3576 3087 3132 8129 3854 1612 3273 839 10907 1781 6216
México 1288 3843 1082 774 2351 1032 5548 2794 243993 1199 9701 19523 20890 8925 1.5e+07 13268 9279 1822 4737 18075 23695 8481 4247 3712 1500 1730 2905 2067 4305 25566 1275 1144
Michoacán de Ocampo 841 4761 824 465 648 3478 2432 1017 11246 472 8345 15010 1241 11555 1.4e+04 4170781 1337 997 861 1045 1074 3162 662 738 1155 1542 929 835 475 2746 365 289
Morelos 283 1532 392 264 494 132 941 543 23318 222 1052 21147 566 1563 1.7e+04 1616 1714221 272 896 1921 6052 901 1306 275 628 405 627 1108 349 2725 419 933
Nayarit 99 4530 753 53 103 738 1829 275 1740 854 1269 2615 289 16900 2.3e+03 1849 275 1069488 282 441 373 249 516 381 3525 1180 91 460 80 1085 171 846
Nuevo León 1406 1320 456 1129 17083 704 13875 4556 9963 4506 3476 4958 7397 5102 1.5e+04 3048 1533 605 5004402 8005 5495 1732 1782 29952 2151 2479 7863 39802 1288 67315 1185 5176
Oaxaca 133 4425 1174 675 665 333 6524 1579 13961 1054 1189 4350 1093 2731 1.9e+04 1131 1265 295 1780 3635167 9530 829 1341 564 1707 1661 1456 1136 448 12920 404 398
Puebla 546 3114 1574 949 1055 276 3990 1929 30356 306 1761 9860 5420 3723 3.0e+04 3213 5381 304 2252 12192 5778654 2046 1770 623 833 1357 2805 3133 9509 35111 1003 467
Querétaro 1362 1448 629 584 933 86 4196 1834 40080 1102 16625 9996 10182 6044 3.6e+04 11354 4703 248 3100 2673 6834 1969009 998 3899 1477 1203 3356 5139 912 13982 628 395
Quintana Roo 212 631 675 8712 864 129 31734 711 20492 124 1636 6198 928 2838 1.2e+04 951 3575 803 2872 3913 4307 1300 1491918 331 953 380 39787 643 686 26063 16058 214
San Luis Potosí 1113 335 104 135 2574 147 571 1198 4288 416 3995 1186 1146 3812 4.4e+03 1362 512 168 11556 713 1360 2141 791 2506515 629 538 188 8889 189 3264 178 2092
Sinaloa 47 13171 5348 124 1032 733 1200 5135 3941 7170 793 5802 292 6604 3.5e+03 2092 301 3918 1117 3427 1184 542 323 460 2657941 10472 548 734 436 2569 222 947
Sonora 80 10457 1459 30 905 427 2890 5611 3652 1165 1242 1812 483 3397 2.9e+03 1415 332 1663 1075 2492 1815 293 136 274 16464 2620188 95 493 133 2453 558 483
Tabasco 77 248 136 6727 428 66 9385 388 1923 152 154 455 277 789 2.2e+03 1245 370 34 1172 1586 1144 185 6234 101 362 171 2136340 1498 77 8064 1889 55
Tamaulipas 241 331 118 1349 2838 294 2585 916 4618 705 2618 807 2208 2218 4.6e+03 1840 876 588 10992 1358 3670 1335 481 7055 488 277 3402 3116244 151 48163 357 688
Tlaxcala 72 780 161 136 245 43 693 186 6561 68 912 709 1777 936 7.5e+03 374 806 52 461 1099 13983 616 487 276 130 273 492 390 1163828 3131 75 38
Veracruz de Ignacio de la Llave 468 3582 1064 3542 2428 910 4601 4072 20427 983 2451 1895 5419 4602 2.4e+04 1605 2384 290 8251 13323 17985 1908 5107 1845 2189 2088 9264 19292 1221 7253629 1533 705
Yucatán 676 829 178 18627 618 162 5320 634 13633 236 674 843 874 1169 8.7e+03 517 1881 119 2521 1477 1901 1623 29293 85 1080 182 16356 892 54 8593 2011796 320
Zacatecas 3407 826 210 95 1762 318 157 2053 1993 2278 1301 406 467 6259 2.0e+03 968 50 537 2075 203 493 504 103 1871 806 250 154 316 218 703 77 1418647
Fuente: Estimaciones del CONAPO.

Gráfico dinámico

Gráfico dinámico de migración reciente a nivel estatal.

#devtools::install_github("mattflor/chorddiag")
require(chorddiag)

load(file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.RData"))

tabla <- Migrantes %>%
          sna::diag.remove(remove.val = 0) 

names <- c("Aguascalientes", "Baja California" ,"Baja California Sur", "Campeche", "Coahuila", "Colima", 
           "Chiapas", "Chihuahua", "Ciudad de México", "Durango", "Guanajuato", "Guerrero", "Hidalgo", "Jalisco",    
           "México", "Michoacán", "Morelos", "Nayarit", "Nuevo León", "Oaxaca", "Puebla", "Querétaro", 
           "Quintana Roo", "San Luis Potosí", "Sinaloa", "Sonora", "Tabasco", "Tamaulipas", "Tlaxcala", 
           "Veracruz", "Yucatán", "Zacatecas")

# Paleta de colores
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")

p <- chorddiag(tabla, 
               groupNames = names,
               groupColors = paleta, 
               groupnamePadding = 10, 
               #height = 700, 
               #width = 700,
               margin = 120,
               groupThickness = 0.07,
               groupPadding = 3,
               groupnameFontsize = 12,
               fadeLevel = '0.1',
               tickInterval = seq(0, 500000, 10000),
               chordedgeColor = "transparent",
               showZeroTooltips = FALSE,
               showTicks = TRUE)

# Ajusta las etiquetas usando JavaScript para modificar su posición
p <- htmlwidgets::onRender(p, '
      function(el, x) {
        d3.selectAll(".group text")
          .attr("text-anchor", "middle")
          .attr("dx", "0")  
          .attr("dy", "0.75em"); 
      }
')

# Crear un contenedor div y aplicar estilos CSS para centrarlo
#p <- tags$div(style = "display: flex; justify-content: center; align-items: center;", p)

p
p %>% 
mapview::mapshot(url = paste0(here::here(),"/images/MR5a_2020.html"))

#htmlwidgets::saveWidget(p, paste0(here::here(), "/Graficos/Estado/01_Migracion reciente/2020/MR5a a nivel estatal 2015 - 2020.html"), selfcontained = TRUE)
#require(webshot)
#webshot(url = paste0(here::here(), "/Graficos/Estado/01_Migracion reciente/2020/MR5a a nivel estatal 2015 - 2020.html"),
 #         file = paste0(here::here(), "/Graficos/Estado/01_Migracion reciente/2020/MR5a a nivel estatal 2015 - 2020.png"),
  #                cliprect = "viewport")

Gráficos migración reciente

ChordDiagram

paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")

load(file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.RData"))

tabla <- Migrantes %>%
          sna::diag.remove(remove.val = 0)  

rownames(tabla) <- stringr::str_wrap(nom_estados, 50)
colnames(tabla) <- stringr::str_wrap(nom_estados, 50)

# Paleta de colores
tabla2 <- color_chord_diagram(tabla1 = tabla, paleta)
#svglite::svglite(paste0(here::here(), "/Graficos/Estado/01_Migracion reciente/2020/ChordDiagram de MR5a a nivel estatal.svg"), width = 20, height = 20)
file = "/Graficos/Estado/01_Migracion reciente/2020/ChordDiagram de MR5a a nivel estatal.pdf"

## Gráficos a nivel estatal 
chord_diagram_graph(file = file, 
                    width = 7, 
                    height = 7, 
                    family = "Montserrat Medium", 
                    paleta = paleta, 
                    tabla1 = tabla, 
                    tabla2 = tabla2, 
                    color_labels = "#000C7D",
                    transparency = 0.4,
                    circo.text = 9,
                    circos.axis.text = 6,
                    adj.text =c(-0.05, 0.5), #Ajuste de las etiquetas (x, y)
                    adj.ylim = 0.1,
                    gap.degree = 2, 
                    clock.wise = FALSE,
                    track.margin = c(-0.07, 0.1),
                    margin = rep(0, 4))

Gráficos por estados

Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.

load(file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.RData"))

tabla <- Migrantes %>%
          sna::diag.remove(remove.val = 0) 

rownames(tabla) <- stringr::str_wrap(nom_estados, 100)
colnames(tabla) <- stringr::str_wrap(nom_estados, 100)

# Nombre de los estados 
estado <- stringr::str_wrap(nom_estados, 100)

filtro_est <- Migrantes %>%
               as.data.frame() %>%
                tibble::rownames_to_column(var = "rn") %>% 
                 melt(., id.vars = "rn", variable.name = "cn") %>%
                  mutate_if(is.factor, as.character) 

### Sacar el promedio de los flujos migratiorios para determinar como se van a grupar los estados 
#### Es importante correr la tabla1[[x]] sin filtros para determinar el número promedio de flujos de migración
#### Filtro <<<<< filter(value > 0 & rn != estado[x]) %>%
#tabla_estados <- sapply(1:32, function(x)
 #                       mean(tail(sort(tabla1[[x]]), 1), na.rm = TRUE))
#p <- data.frame(estados = est,
 #               filtro_estados = filtro_mig)
#write.table(p, file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Filtro a nivel estatal.txt"))
#write.xlsx(p, file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Filtro a nivel estatal.xlsx"))

filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Filtro a nivel estatal.xlsx"), colNames = TRUE) %>% 
               pull(filtro_estados)

tabla1 <- migration_flows_states(tabla = tabla, 
                                 filtro_mig = filtro_mig, 
                                 filtro_est = filtro_est, 
                                 category = estado, 
                                 group = "Otro estados")

## Se guardan las matrices de migración reciente para analizarlos después. 
wb <- createWorkbook()
for(i in 1:32){
     tabla <- tabla1[[i]] %>%
                as.data.frame() %>%
                 adorn_totals(c("row", "col"), 
                               fill = "-", 
                                na.rm = TRUE, 
                                 name = "Total"
                              ,,,,contains(colnames(tabla1[[i]])))
                 
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/Matriz MR5a por estados_2015 -2020_Reduccion.xlsx"), 
               overwrite = TRUE)
}
saveRDS(tabla1, file = paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/Tabla MR5a a por estados.RDS"))
tabla1 <- readRDS(file = paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/Tabla MR5a a por estados.RDS"))

total_tablas <- totales(tabla1 = tabla1, 
                        Clave = "CVE_ENT", 
                        Inmigrantes = "Inmigrantes",  
                        Emigrantes = "Emigrantes")

porcentajes_tablas <- porcentajes(tabla1 = tabla1, 
                                  Clave = "CVE_ENT", 
                                  Inmigrantes = "%Inmigrantes",  
                                  Emigrantes = "%Emigrantes")

# Se guardan los totales de las matrices reducidas 
wb <- createWorkbook()
for(i in 1:32){
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, total_tablas[[i]], colNames = TRUE, startCol = 1)
     writeData(wb, i, porcentajes_tablas[[i]], colNames = TRUE, startCol = 5)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/Matriz MR5a por estados_2015 -2020_Reduccion_Totales.xlsx"), 
               overwrite = TRUE)
}
tabla1 <- readRDS(file = paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/Tabla MR5a a por estados.RDS"))

#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636")

tabla2 <- color_chord_diagram(tabla1 = tabla1, paleta)
file = "/Graficos/Estado/01_Migracion reciente/2020/ChordDiagram de MR5a desagregado por estado.pdf"

## Gráficos a nivel estatal 
chord_diagram_graph(file = file, 
                    width = 8, 
                    height = 8, 
                    family = "Montserrat Medium", 
                    paleta = paleta, 
                    tabla1 = tabla1, 
                    tabla2 = tabla2, 
                    color_labels = "#000C7D",
                    transparency = 0,
                    circo.text = 9,
                    circos.axis.text = 6,
                    adj.text =c(-0.05, 0.5), #Ajuste de las etiquetas (x, y)
                    adj.ylim = 0.1,
                    gap.degree = 2, 
                    clock.wise = FALSE,
                    track.margin = c(-0.07, 0.1),
                    margin = rep(0, 4))

Etiquetas

file = "/Graficos/Estado/01_Migracion reciente/2020/Etiquetas a nivel estatal.pdf"

labels_chord_diagram(file = file, 
                     width = 7, 
                     height = 8, 
                     family = "Montserrat Medium", 
                     paleta = paleta, 
                     tabla1 = tabla1, 
                     labels = nom_estados)

Gráfico Sankey

load(file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.RData"))

Migrantes <- Migrantes %>%
              sna::diag.remove(remove.val = 0)  

rownames(Migrantes) <- stringr::str_wrap(nom_estados, 50)
colnames(Migrantes) <- stringr::str_wrap(nom_estados, 50)

# Matiz migración interna (Población de 5 años y más)
tabla <- Migrantes %>% 
          as.data.frame() %>%
           tibble::rownames_to_column(var = "rn") %>%
            melt(., id.vars = "rn", variable.name = "cn") %>%
             as_tibble() %>%
              mutate(rn = forcats::fct_relevel(.$rn, nom_estados),
                     cn = forcats::fct_relevel(.$cn, nom_estados)) %>%
               filter(value >= 0)  
p <- tabla %>% 
       ggplot(aes(axis1 = rn, 
                   axis2 = cn, 
                    y = value),  # c("value", "freq", "tasa")
               reverse = FALSE, 
                na.rm = TRUE) +
        geom_alluvium(aes(fill = rn),
                       curve_type = "quintic", 
                        color = "transparent", 
                         alpha = 0.85, 
                          lwd = 0.001, 
                           width = 1/5,
                            reverse = FALSE) +
         geom_stratum(aes(fill = cn), 
                       color = "white", 
                        alpha = 0.65,  
                         lwd = 0.001, 
                          width = 1/5,
                           reverse = FALSE) +
          geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""), 
                               fontface =  ifelse(after_stat(x) == 1, 'bold', 'plain')),
                           stat = "stratum", 
                            size = 3, 
                             direction = "y", 
                              nudge_x = -.3,
                               min.segment.length = unit(1, "lines"),
                                force = 1,
                                 force_pull = 0,
                                  family = "montserrat",
                                   reverse = FALSE) +
           geom_text_repel(aes(label = ifelse(after_stat(x)  == 2, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""),
                               fontface =  ifelse(after_stat(x) == 2, 'bold', 'plain')),
                            stat = "stratum", 
                             size = 3,
                              direction = "y", 
                               nudge_x = .3, 
                                force = 1,
                                 force_pull = 0,
                                  family = "montserrat",
                                   reverse = FALSE) +
            theme_void() + 
             theme(plot.margin = margin(t = 1, r = 1, b = 1, l = 1, "cm"),
                    text = element_text(family = "montserrat"),
                     axis.text = element_blank(),
                      axis.title = element_blank(),
                       strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
                        legend.key.size = unit(0.5, "cm"),
                         legend.text = element_text(size = 9, family = "montserrat")) + 
              scale_x_discrete(expand = c(0, 0.4)) +
               scale_fill_viridis_d(option = "A", end = 0.9, begin = 0.2) +
                guides(fill = guide_legend(ncol = 1, na.translate = F)) + 
                 labs(fill = "", 
                       color = "")
p
path = paste0(here::here(), "/Graficos/Estado/01_Migracion reciente/2020/GSankey de MR5a a nivel estatal.pdf")
ggexport(p, width = 14, height = 10, dpi = 400, filename = path)

Desagregado por estados

load(file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.RData"))

# Se cambian las equiquetas de los estados 
estados <- est 

rownames(Migrantes) <- estados
colnames(Migrantes) <- estados

tabla <- Migrantes %>%
          sna::diag.remove(remove.val = 0) %>%
           as.data.frame() %>%
            tibble::rownames_to_column(var = "rn") %>% 
             melt(., id.vars = "rn", variable.name = "cn") %>%
              mutate_if(is.factor, as.character) %>%
               mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% estados | .$cn %in% estados), value, 0)) %>%
                mutate(rn = forcats::fct_relevel(.$rn, estados),
                       cn = forcats::fct_relevel(.$cn, estados)) 
p <- lapply(1:32, function(x){
                   tabla <- tabla %>%
                             mutate(rn = forcats::fct_relevel(.$rn, estados),
                                    cn = forcats::fct_relevel(.$cn, estados)) %>%
                              mutate(value = ifelse(.$rn %in% estados[x] | .$cn %in% estados[x], value, 0)) 

                    tabla %>% 
                     ggplot(aes(axis1 = rn, 
                                 axis2 = cn, 
                                  y = value),  # c("value", "freq", "tasa")
                             reverse = FALSE, 
                              na.rm = TRUE) + 
                      geom_alluvium(aes(fill = rn),  
                                     color = "transparent", 
                                      alpha = 0.8, 
                                       lwd = 0.001, 
                                        width = 1/5,
                                         reverse = FALSE) +
                       geom_stratum(aes(fill = rn), 
                                     color = "#F1F1F1", 
                                      alpha = 1, 
                                       lwd = 0.001, 
                                        width = 1/5,
                                         reverse = FALSE) +
                         geom_text_repel(aes(label = ifelse(after_stat(x)  == 1, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""),
                                             fontface =  ifelse(after_stat(x) == 1, 'bold', 'plain')),
                                           stat = "stratum", 
                                            size = 3,
                                             direction = "y", 
                                              nudge_x = -.2, 
                                               force = 1,
                                                        force_pull = 0,
                                                         family = "montserrat",
                                                          reverse = FALSE) +
                          geom_text_repel(aes(label = ifelse(after_stat(x)  == 2, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""),
                                              fontface =  ifelse(after_stat(x) == 2, 'bold', 'plain')),
                                           stat = "stratum", 
                                            size = 3,
                                             direction = "y", 
                                              nudge_x = .2, 
                                               force = 1,
                                                force_pull = 0,
                                                 family = "montserrat",
                                                  reverse = FALSE) +
                            theme_void() + 
                             theme(plot.margin = margin(t = 1, r = 1.5, b = 1, l = 0, "cm"),
                                    text = element_text(family = "montserrat"),
                                     axis.text = element_blank(),
                                      axis.title = element_blank(),
                                       strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
                                        legend.key.size = unit(0.5, "cm"),
                                         legend.text = element_text(size = 9, family = "montserrat"),
                                          legend.position = c(1, .5)) + 
                              scale_x_discrete(expand = c(-0.1, 0.35)) +
                               scale_fill_viridis_d(option = "A", end = 0.9, begin = 0.2) +
                                guides(fill = guide_legend(ncol = 1, na.translate = F)) + 
                                 labs(fill = "", 
                                      color = "")
              }
        )

path = paste0(here::here(), "/Graficos/Estado/01_Migracion reciente/2020/GSankey de MR5a desagregado por estados_Absolutos.pdf")
ggexport(list = p, width = 14, height = 10, dpi = 400, filename = path)

Indicadores

Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta

Opción 1

# Clave de los estados
tablas <- str_pad(rep(1:32), width = 3, pad = "0")

# Diseño muestral
MC <- mydata  %>%
       select(FACTOR, ESTRATO, UPM, CVE_ENT, ENT, ENT_PAIS_RES_5A, EDAD) %>%
        as.data.frame() %>%
         mutate(EDAD = as.numeric(.$EDAD)) %>%
          mutate(ENT_PAIS_RES_5A = case_when(.$ENT_PAIS_RES_5A %in% tablas ~.$ENT_PAIS_RES_5A,
                                             .$ENT_PAIS_RES_5A %nin% tablas ~ "888", #Residencia en otro país
                                             .$ENT_PAIS_RES_5A %in% "997" ~ "999",
                                             .$ENT_PAIS_RES_5A %in% "998" ~ "998",
                                             .$ENT_PAIS_RES_5A %in% "997" ~ "999")) %>%
          # Se reclasifica la variable de residencia hace 5 años
           mutate(ENT_PAIS_RES_5A = case_when(.$ENT_PAIS_RES_5A %in% tablas ~.$ENT_PAIS_RES_5A)) %>%
            mutate(M = 1) %>% # Residentes 
             mutate(Pob.5ymas = ifelse(EDAD >= 5 & EDAD <= 130, 1, 0),
                    Migrantes = case_when(.$CVE_ENT !=.$ENT_PAIS_RES_5A  & (EDAD >= 5 & EDAD <= 130) ~ 1),
                    Residentes = case_when(.$CVE_ENT ==.$ENT_PAIS_RES_5A & (EDAD >= 5 & EDAD <= 130) ~ 1)) %>% 
              select(FACTOR, ESTRATO, UPM, CVE_ENT, ENT_PAIS_RES_5A, M, Pob.5ymas,  Migrantes, Residentes) %>%
               melt(., id = c("UPM", "ESTRATO", "FACTOR", "M", "Pob.5ymas",  "Migrantes", "Residentes")) %>%
                srvyr::as_survey_design(ids = UPM, strata = ESTRATO, weights = FACTOR, nest = TRUE)

tabla <- MC %>%
          group_by(value) %>%
           dplyr::summarise(Pob_Total = survey_total(ifelse(variable == "CVE_ENT", M, 0), na.rm = TRUE)),           
                            Pob.5ymas = survey_total(ifelse(variable == "CVE_ENT", Pob.5ymas, 0), na.rm = TRUE),
                            Residentes = survey_total(ifelse(variable == "CVE_ENT", Residentes, 0), na.rm = TRUE),
                            Inmigrantes = survey_total(ifelse(variable == "CVE_ENT", Migrantes, 0), na.rm = TRUE), 
                            Emigrantes = survey_total(ifelse(variable == "ENT_PAIS_RES_5A" & value %in% tablas, Migrantes, 0), na.rm = TRUE)) %>%
            ungroup()  %>%
             mutate(Mig.Neta = Inmigrantes - Emigrantes, 
                    MIGBRUTA = Inmigrantes + Emigrantes,
                    TASAINMI = ((Inmigrantes/ 5) /((Pob_Total + Pob.5ymas) / 2))*1000,
                    TASAEMI = ((Emigrantes/ 5) /((Pob_Total + Pob.5ymas) / 2))*1000, 
                    TASAMIG = TASAINMI - TASAEMI,
                    EFICACIA = MIGNETA - MIGBRUTA)

#write.xlsx(p, file =  "Output/Estado/01_Migracion reciente/2020/Indicadores de MR5a por estado 2015 - 2020.xlsx", overwrite = TRUE)

Opcion 2

Se trabaja con la matriz cuadrada, la cual no se satura la computadora.

################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
              as.data.frame() %>%
               group_by(CVE_ENT) %>%
                summarise(Pob.Total = sum(FACTOR)) 

################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
              as.data.frame() %>%
               mutate(EDAD = as.numeric(.$EDAD)) %>%
                subset(EDAD >= 5 & EDAD <=130) %>%
                 group_by(CVE_ENT) %>%
                  summarise(Pob.5ymas = sum(FACTOR)) 

################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Bases/Estado/01_Migracion reciente/2020/Matriz de migracion reciente a nivel estatal 2015-2020.RData"))

rownames(Migrantes) <- str_pad(rep(1:32), width = 3, pad = "0")
colnames(Migrantes) <- str_pad(rep(1:32), width = 3, pad = "0")
  
Residentes <- Migrantes %>%
               rownames_to_column() %>%
                tidyr::gather(CVE_ENT, Value, -rowname)%>%
                 filter(rowname == CVE_ENT) %>%
                  select(-rowname) %>%
                   droplevels() %>%
                    rename("Residentes" = "Value") 

################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- Migrantes %>% 
                sna::diag.remove(remove.val = 0) %>%
                 as.data.frame() %>%
                  tibble::rownames_to_column(var = "CVE_ENT") %>%
                   melt(., id.vars = "CVE_ENT", variable.name = "ENT_PAIS_RES_5A") %>%
                    mutate_at(vars(3), as.numeric) %>%
                     as_tibble() %>%
                      filter(CVE_ENT != ENT_PAIS_RES_5A) %>%
                       group_by(CVE_ENT) %>%
                        summarise(Inmigrantes = sum(value, na.rm = TRUE))

################################################################################
############################### Emigrantes #####################################
Emigrantes <- Migrantes %>% 
               sna::diag.remove(remove.val = 0) %>%
                as.data.frame() %>%
                 tibble::rownames_to_column(var = "CVE_ENT") %>%
                  melt(., id.vars = "CVE_ENT", variable.name = "ENT_PAIS_RES_5A") %>%
                   mutate_at(vars(3), as.numeric) %>%
                    as_tibble() %>%
                     filter(CVE_ENT != ENT_PAIS_RES_5A) %>%
                      group_by(ENT_PAIS_RES_5A) %>%
                       summarise(Emigrantes = sum(value, na.rm = TRUE)) %>%
                        rename("CVE_ENT" = "ENT_PAIS_RES_5A") 

tabla <- Pob.Total %>%
          left_join(., Pob.5ymas, by = c("CVE_ENT")) %>%
          left_join(., Residentes, by = c("CVE_ENT")) %>%
          left_join(., Inmigrantes, by = c("CVE_ENT")) %>%
          left_join(., Emigrantes, by = c("CVE_ENT")) %>%
           mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
                  Mig.Bruta = .$Inmigrantes + .$Emigrantes, 
                  Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2))*1000,
                  Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2))*1000,
                  Tasa.Mig = Tasa.Inmig - Tasa.Emig, 
                  Eficacia = Mig.Neta - Mig.Bruta)

write.xlsx(tabla, file = paste0(here::here(), "/Output/Estado/01_Migracion reciente/2020/Indicadores de MR5a por estado 2015 - 2020.xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Indicadores de MR5a por estado 2015 - 2020.RData"))
Indicadores de migración reciente
Nivel estatal
Clave de la entidad Pob.Total Pob.5ymas Residentes Inmigrantes Emigrantes Mig.Neta Mig.Bruta Tasa.Inmig Tasa.Emig Tasa.Mig Eficacia
001 1 421 198 1 296 230 1 226 454 60 587 24 773 35 814 85 360 8.9 3.6 5.3 −49 546
002 3 739 797 3 464 891 3 155 213 249 358 91 483 157 875 340 841 13.8 5.1 8.8 −182 966
003 793 424 729 216 655 361 68 474 32 497 35 977 100 971 18.0 8.5 9.5 −64 994
004 926 858 848 445 795 746 35 515 51 028 −15 513 86 543 8.0 11.5 −3.5 −102 056
005 3 137 032 2 855 617 2 761 903 77 049 63 950 13 099 140 999 5.1 4.3 0.9 −127 900
006 728 300 674 851 633 038 37 334 29 755 7 579 67 089 10.6 8.5 2.2 −59 510
007 5 524 506 4 938 226 4 849 943 55 455 167 279 −111 824 222 734 2.1 6.4 −4.3 −334 558
008 3 725 058 3 427 399 3 287 846 108 985 65 187 43 798 174 172 6.1 3.6 2.4 −130 374
009 9 159 393 8 675 781 8 274 545 338 196 566 115 −227 919 904 311 7.6 12.7 −5.1 −1 132 230
010 1 821 279 1 652 979 1 598 892 43 652 56 889 −13 237 100 541 5.0 6.5 −1.5 −113 778
011 6 144 449 5 598 366 5 459 604 104 757 93 369 11 388 198 126 3.6 3.2 0.4 −186 738
012 3 525 695 3 187 065 3 111 059 61 652 187 853 −126 201 249 505 3.7 11.2 −7.5 −375 706
013 3 075 237 2 836 379 2 665 783 156 179 87 173 69 006 243 352 10.6 5.9 4.7 −174 346
014 8 303 835 7 626 435 7 375 857 198 977 162 934 36 043 361 911 5.0 4.1 0.9 −325 868
015 16 943 627 15 661 775 15 154 946 450 751 520 719 −69 968 971 470 5.5 6.4 −0.9 −1 041 438
016 4 728 162 4 298 137 4 170 781 94 667 116 732 −22 065 211 399 4.2 5.2 −1.0 −233 464
017 1 961 694 1 816 739 1 714 221 90 214 66 942 23 272 157 156 9.6 7.1 2.5 −133 884
018 1 226 179 1 123 645 1 069 488 46 167 41 791 4 376 87 958 7.9 7.1 0.7 −83 582
019 5 768 781 5 314 945 5 004 402 270 744 93 008 177 736 363 752 9.8 3.4 6.4 −186 016
020 4 113 433 3 751 736 3 635 167 95 409 136 116 −40 707 231 525 4.9 6.9 −2.1 −272 232
021 6 567 595 5 980 199 5 778 654 176 573 161 667 14 906 338 240 5.6 5.2 0.5 −323 334
022 2 362 209 2 176 868 1 969 009 192 057 61 615 130 442 253 672 16.9 5.4 11.5 −123 230
023 1 852 929 1 702 045 1 491 918 191 167 82 718 108 449 273 885 21.5 9.3 12.2 −165 436
024 2 815 438 2 585 174 2 506 515 60 020 68 051 −8 031 128 071 4.4 5.0 −0.6 −136 102
025 2 986 880 2 751 749 2 657 941 84 168 108 843 −24 675 193 011 5.9 7.6 −1.7 −217 686
026 2 924 652 2 703 869 2 620 188 66 694 65 096 1 598 131 790 4.7 4.6 0.1 −130 192
027 2 397 125 2 188 340 2 136 340 47 562 120 484 −72 922 168 046 4.1 10.5 −6.4 −240 968
028 3 518 497 3 244 697 3 116 244 108 145 109 605 −1 460 217 750 6.4 6.5 −0.1 −219 210
029 1 340 912 1 228 497 1 163 828 43 476 29 192 14 284 72 668 6.8 4.5 2.2 −58 384
030 8 046 861 7 444 681 7 253 629 169 749 384 450 −214 701 554 199 4.4 9.9 −5.5 −768 900
031 2 317 135 2 143 352 2 011 796 120 114 38 860 81 254 158 974 10.8 3.5 7.3 −77 720
032 1 617 384 1 464 173 1 418 647 32 832 50 505 −17 673 83 337 4.3 6.6 −2.3 −101 010
Fuente: Estimaciones del CONAPO.

Nivel municipal

Migración reciente

Matrices

### Municipios que no pertenecen en la muestra
municipios_nomuestra <- c("004012", "007125", "029048")

#Clave de los municipios 2020 
municipios <- MUN %>%
               select(CVE_MUN) %>%
                unique() %>%
                 filter(CVE_MUN %nin% municipios_nomuestra) %>%
                  pull(CVE_MUN)

Pob.5ymas <- mydata %>%
              mutate(CVE_MUN_RES = paste0(ENT_PAIS_RES_5A, MUN_RES_5A)) %>%
               mutate(ENT_PAIS_RES_5A = case_when(.$ENT_PAIS_RES_5A %in% estados ~.$ENT_PAIS_RES_5A,
                                                  .$ENT_PAIS_RES_5A %nin% estados ~ "888", #Residencia en otro país
                                                  .$ENT_PAIS_RES_5A %in% "997" ~ "997",
                                                  .$ENT_PAIS_RES_5A %in% "998" ~ "998",
                                                  .$ENT_PAIS_RES_5A %in% "997" ~ "999"),
                      CVE_MUN_RES = case_when(.$CVE_MUN_RES %in% municipios ~.$CVE_MUN_RES,
                                              #### Se excluyen los municipios que no fueron muestreados
                                              .$CVE_MUN_RES %in% municipios_nomuestra ~ "777",
                                               nchar(.$CVE_MUN_RES) == 3 ~ "888",  #Residencia en otro país
                                              .$CVE_MUN_RES %in% "997999" ~ '997999',
                                              .$ENT_PAIS_RES_5A %in% "997" ~ "997",
                                              .$ENT_PAIS_RES_5A %in% "998" ~ "998",
                                              .$ENT_PAIS_RES_5A %in% "999" ~ "999",
                                              .$MUN_RES_5A %in% "999" ~ "999")) %>%
                 mutate(EDAD = as.numeric(.$EDAD)) %>%
                  subset(EDAD >= 5 & EDAD <= 130) %>%
                   select(FACTOR, ESTRATO, UPM, CVE_MUN, CVE_MUN_RES, EDAD) %>%
                    filter(CVE_MUN_RES %in% municipios) 

MC <- Pob.5ymas %>%
       svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)

saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/MC_municipal.RDS"))
MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/MC_municipal.RDS"))

Migrantes <- svytable(~CVE_MUN_RES + CVE_MUN, design = MC) 

Se genera la matriz cuadrada y se le asignan los nombres de los estados.

Migrantes <- Migrantes %>%
              as.data.frame() %>%
               expss::cross_cases(CVE_MUN, CVE_MUN_RES, weight = Freq) %>%
                as.data.frame() %>%
                 rename("CVE_MUN" = "row_labels") %>% 
                  arrange(CVE_MUN) %>%
                   slice(-1) 

rownames <- Migrantes %>% 
             mutate(CVE_MUN = substr(.$CVE_MUN, 9, 16)) %>% 
              pull(CVE_MUN)

colnames <- names(Migrantes) %>% 
             as.data.frame() %>% 
              slice(-1) %>% 
               rename("CVE_MUN" = ".") %>%
                mutate(`CVE_MUN` = substr(.$CVE_MUN, 13, 20)) %>%
                 pull(CVE_MUN)

# Se elimina la variable CVE_MUN
Migrantes <- Migrantes %>%
              select(-CVE_MUN)

rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames

saveRDS(Migrantes, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel municipal.RDS"))
save(Migrantes, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel municipal.RData"))

require(openxlsx)
wb <- createWorkbook()
addWorksheet(wb, "M.Reciente")
writeData(wb, 1, Migrantes %>% as.data.frame() %>% tibble::rownames_to_column(var = "CVE_MUN"), colNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel municipal.xlsx"), overwrite = TRUE)

Matriz de migración reciente hace 5 años a nivel municipal, 2015 - 2020

Matriz de migración reciente 2015 - 2020
Nivel municipal
CVE_MUN 001001 001002 001003 001004 001005 001006 001007 001008 001009 001010 001011 002001 002002 002003 002004 002005 002006 003001 003002 003003 003008 003009 004001 004002 004003 004004 004005 004006 004007
001001 811844 835 491 0 1335 440 550 177 177 571 655 183 426 0 120 59 0 0 0 177 0 0 0 0 0 0 0 0 0
001002 330 44223 10 5 28 15 33 0 72 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001003 139 0 50577 0 20 0 21 0 0 0 0 4 6 6 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0
001004 75 3 0 14634 16 3 80 0 18 0 2 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0
001005 3166 199 19 34 107678 98 46 30 89 46 201 0 9 0 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001006 199 5 36 5 177 40671 234 2 50 0 358 0 4 0 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001007 238 13 25 135 33 93 49027 26 82 0 61 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001008 41 2 2 0 20 28 12 8174 35 0 8 4 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001009 75 31 0 15 0 73 153 0 19446 0 41 0 8 3 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001010 219 18 0 0 5 0 2 0 0 17365 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001011 2240 203 160 2 536 523 21 92 67 5 46088 0 0 0 0 156 0 0 0 0 0 0 0 0 0 0 0 0 0
002001 0 0 14 0 0 0 0 0 0 0 0 376054 1737 214 2213 115 0 449 1032 701 245 56 0 0 0 0 0 0 0
002002 174 67 0 0 0 0 0 0 0 0 0 1139 929519 755 1677 347 0 0 48 79 150 0 0 0 0 0 0 0 0
002003 70 0 0 0 0 0 0 0 0 0 0 52 865 83911 1214 49 0 25 0 0 97 0 0 0 0 0 0 0 0
002004 165 0 0 0 0 0 0 0 0 0 0 1433 2600 1716 1546345 2894 0 0 648 965 913 0 0 165 0 0 0 0 0
002005 0 0 0 0 0 0 0 0 0 0 0 176 307 149 1299 101415 0 0 0 65 25 0 0 24 37 0 0 0 0
002006 0 0 0 0 0 0 0 0 0 0 0 0 82 0 267 0 91557 0 460 70 150 66 0 0 0 0 0 0 0
003001 0 0 0 0 0 0 0 0 0 0 0 39 59 0 28 0 0 64658 234 496 297 137 0 8 0 0 0 0 0
003002 0 0 0 0 0 0 0 0 0 0 0 786 225 11 119 18 0 135 54515 173 43 22 0 0 0 0 0 0 0
003003 20 0 0 0 0 0 0 0 0 0 0 494 225 0 643 0 0 1741 1082 249997 2700 348 0 0 0 0 0 0 0
003008 119 0 0 0 0 0 0 0 0 0 0 272 174 26 426 221 0 237 287 981 260221 185 0 76 0 28 0 0 0
003009 0 0 0 0 0 0 0 0 0 0 0 26 12 0 63 0 0 98 26 169 12 15609 0 0 0 0 0 0 0
004001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52125 284 96 137 122 83 0
004002 17 0 0 0 0 0 0 0 0 0 0 40 0 0 51 0 0 0 49 0 0 0 182 257197 1080 736 120 494 54
004003 32 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 81 525 210329 452 0 0 33
004004 0 0 0 0 0 0 0 0 0 0 0 36 0 0 7 0 0 0 0 0 0 0 0 217 469 67521 0 0 0
004005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 20 139 9 56 28556 26 0
004006 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 92 0 7 5 36662 0
004007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 33 2 0 0 7728
004008 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 5 139 12 2 22 5 2
Fuente: Estimaciones del CONAPO.

Gráficos

Gráficos por municipios

Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.

# Matriz cuadrada a nivel municipal 
load(paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel municipal.RData"))

rownames <- rownames(Migrantes) %>% 
             as.data.frame() %>%
              rename("CVE_MUN" = ".") %>%
               left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
                mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(CVE_MUN)

colnames <- colnames(Migrantes) %>% 
             as.data.frame() %>%
              rename("CVE_MUN" = ".") %>%
               left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
                mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(CVE_MUN)

rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames

# Nombre de los estados 
estado <- stringr::str_wrap(nom_estados, 100)

# Clave de los municipios 
### Municipios que no pertenecen en la muestra
municipios_nomuestra <- c("004012", "007125", "029048")

municipios <- MUN %>% 
               filter(CVE_MUN %nin% municipios_nomuestra) %>%
                mutate(municipios = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(municipios)
             
################################################################################
################################## Filtro ######################################
Inmigrantes <- Inmigrantes_function(municipios, Migrantes)

Emigrantes <- Emigrantes_function(municipios, Migrantes)
   
################################## Filtro ######################################
filtro  <- Inmigrantes %>%
            full_join(., Emigrantes, by = c("rn" = "cn")) %>%
             mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE))

filtro_est <- Migrantes %>%
               as.data.frame() %>%
                tibble::rownames_to_column(var = "rn") %>% 
                 melt(., id.vars = "rn", variable.name = "cn") %>%
                  mutate_if(is.factor, as.character) %>%
                   filter(value > 0)

### Sacar el promedio de los flujos migratiorios para determinar como se van a grupar los estados 
#### Es importante correr la tabla1[[x]] sin filtros para determinar el número promedio de flujos de migración
#### Filtro <<<<< filter(value > 0 & rn != estado[x]) %>%
#### Se anexa el filtro de estados filter(value > 100000000 & rn != estado[x]) %>% 
#### De esta manera solo se contemplan a los municipios

#p <- data.frame(estados = est,
 #               filtro_municipio = filtro_mig,
  #              filtro_estado = filtro_out)
#write.xlsx(p, file = paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel municipal.xlsx"), overwrite = TRUE)

#### Filtro de municipios
filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel municipal.xlsx"), colNames = TRUE) %>%
               pull(filtro_municipio)

#### Filtro de estados 
filtro_out <- read.xlsx(paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel municipal.xlsx"), colNames = TRUE) %>%
               pull(filtro_estado)

tabla <- Migrantes %>%
          as.data.frame() %>%
           tibble::rownames_to_column(var = "rn") %>%
            melt(., id.vars = "rn", variable.name = "cn") %>%
             mutate_if(is.factor, as.character) %>%
              filter(value > 0)

## Se generan los filtros correspondientes a la matriz cuadrada por estados 
tabla1 <- migration_flows_municipality(tabla = tabla, 
                                       filtro_mun = filtro,
                                       filtro_est = filtro_est,
                                       filtro_mig = filtro_mig, 
                                       filtro_out = filtro_out, 
                                       category_group = estados, 
                                       category_names = nom_estados,
                                       group_mun = "Otros municipios",
                                       group_est = "Otros estados")

## Se sacan los flujos migratorios que pertencen a otros estados
#tabla_estados <- sapply(1:32, function(i){
#                         tabla1[[i]] %>%
#                          as.data.frame() %>%
#                           adorn_totals(c("row", "col"), 
#                                         fill = "-", 
#                                          na.rm = TRUE, 
#                                           ,,,,contains(colnames(tabla1[[i]]))) %>% 
#                            select(`Otros estados`) %>%
#                             slice(nrow(.)) %>%
#                              mutate(`Otros estados` = .$`Otros estados`/30) %>%
#                               pull(`Otros estados`)
#  }
#)

## Se sacan los flujos migratorios que pertencen a otros municipios
#tabla_municipios <- sapply(1:32, function(i){
#                               tabla1[[i]] %>%
#                                as.data.frame() %>%
#                                 select(-c(`Otros estados`)) %>%
#                                  adorn_totals(c("row", "col"), 
#                                                fill = "-", 
#                                                 na.rm = TRUE, 
#                                                  ,,,,contains(colnames(tabla1[[i]])))  N%>% 
#                                    slice(nrow(.)) %>%
#                                     mutate(Total = .$Total/50) %>%
#                                      pull(Total)
#})

## Se guardan las matrices de migración reciente para analizarlos después. 
wb <- createWorkbook()
for(i in 1:32){
     tabla <- tabla1[[i]] %>%
                as.data.frame() %>%
                 adorn_totals(c("row", "col"), 
                               fill = "-", 
                                na.rm = TRUE, 
          ,,,,contains(colnames(tabla1[[i]])))
                 
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz MR5a nivel municipal_Reduccion.xlsx"), 
               overwrite = TRUE)
}

saveRDS(tabla1, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel municipal.RDS"))
tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel municipal.RDS"))

total_tablas <- totales(tabla1 = tabla1, 
                        Clave = "CVE_MUN", 
                        Inmigrantes = "Inmigrantes",  
                        Emigrantes = "Emigrantes")

porcentajes_tablas <- porcentajes(tabla1 = tabla1, 
                                  Clave = "CVE_MUN", 
                                  Inmigrantes = "%Inmigrantes",  
                                  Emigrantes = "%Emigrantes")

# Se guardan los totales de las matrices reducidas 
wb <- createWorkbook()
for(i in 1:32){
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, total_tablas[[i]], colNames = TRUE, startCol = 1)
     writeData(wb, i, porcentajes_tablas[[i]], colNames = TRUE, startCol = 5)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz MR5a nivel municipal_Reduccion_Totales.xlsx"), 
               overwrite = TRUE)
}

Dada la magnitud de municipios en algunos estados se seleccionan solo algunos de ellos.

tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel municipal.RDS"))

## Genera una lista de vectores con los colores a nivel municipal 
#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")

tabla2 <- color_chord_diagram(tabla1 = tabla1, paleta)
file = "/Graficos/Municipio/01_Migracion reciente/2020/ChordDiagram de MR5a desagregado por estado.pdf"

## Gráficos a nivel municipal
chord_diagram_graph(file = file, 
                    width = 15, 
                    height = 10, 
                    family = "Montserrat Medium", 
                    paleta = paleta, 
                    tabla1 = tabla1, 
                    tabla2 = tabla2, 
                    color_labels = "#000C7D",
                    transparency = 0,
                    circo.text = 9,
                    circos.axis.text = 6,
                    adj.text =c(-0.05, 0.5), #Ajuste de las etiquetas (x, y)
                    adj.ylim = 0.1,
                    gap.degree = 2, 
                    clock.wise = FALSE,
                    track.margin = c(-0.07, 0.1),
                    margin = rep(0, 4))

Etiquetas

file =  "/Graficos/Municipio/01_Migracion reciente/2020/Etiquetas a nivel municipal.pdf-"

labels_chord_diagram(file = file, 
                     width = 7, 
                     height = 10, 
                     family = "Montserrat Medium", 
                     paleta = paleta, 
                     tabla1 = tabla1, 
                     labels = nom_estados)

Indicadores

Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración Bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta

Dada la cantidad de datos a calcular para la obtención de los indicadores. La obtención de los indicadores se realiza de manera directa, es decir, partiendo de la matriz cuadrada original.

################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
              as.data.frame() %>%
               group_by(CVE_MUN) %>%
                summarise(Pob.Total = sum(FACTOR)) 

################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
              as.data.frame() %>%
               mutate(EDAD = as.numeric(.$EDAD)) %>%
                subset(EDAD >= 5 & EDAD <=130) %>%
                 group_by(CVE_MUN) %>%
                  summarise(Pob.5ymas = sum(FACTOR)) 

################################################################################
########################### Residentes #########################################
load(paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel municipal.RData"))

Residentes <- Migrantes %>%
               rownames_to_column() %>% 
                gather(CVE_MUN, Value, -rowname)%>%
                 filter(rowname == CVE_MUN) %>%
                  select(-rowname) %>%
                   droplevels() %>%
                    rename("Residentes" = "Value") 

################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- Migrantes %>% 
                as.data.frame() %>%
                 tibble::rownames_to_column(var = "CVE_MUN") %>%
                  melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
                   mutate_at(vars(3), as.numeric) %>%
                    as_tibble() %>%
                     filter(CVE_MUN != CVE_MUN_RES) %>%
                      group_by(CVE_MUN) %>%
                       summarise(Inmigrantes = sum(value, na.rm = TRUE))

################################################################################
############################### Emigrantes #####################################
Emigrantes <- Migrantes %>% 
               as.data.frame() %>%
                tibble::rownames_to_column(var = "CVE_MUN") %>%
                 melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
                  mutate_at(vars(3), as.numeric) %>%
                   as_tibble() %>%
                    filter(CVE_MUN != CVE_MUN_RES) %>%
                     group_by(CVE_MUN_RES) %>%
                      summarise(Emigrantes = sum(value, na.rm = TRUE)) %>%
                       rename("CVE_MUN" = "CVE_MUN_RES") 

tabla <- Pob.Total %>%
          left_join(., Pob.5ymas, by = c("CVE_MUN")) %>%
          left_join(., Residentes, by = c("CVE_MUN")) %>%
          left_join(., Inmigrantes, by = c("CVE_MUN")) %>%
          left_join(., Emigrantes, by = c("CVE_MUN")) %>%
            mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
                   Mig.Bruta = .$Inmigrantes + .$Emigrantes, 
                   Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2))*1000,
                   Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2))*1000,
                   Tasa.Mig = Tasa.Inmig - Tasa.Emig, 
                   Eficacia = Mig.Neta - Mig.Bruta)

write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Indicadores de migracion reciente a nivel municipal 2015 - 2020.xlsx"), overwrite = TRUE)

save(tabla, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Indicadores de migracion reciente a nivel municipal 2015 - 2020.RData"))
Indicadores de migración reciente
Nivel municipal
Clave del municipio Pob.Total Pob.5ymas Residentes Inmigrantes Emigrantes Mig.Neta Mig.Bruta Tasa.Inmig Tasa.Emig Tasa.Mig Eficacia
001001 945 506 869 741 811 844 47 898 26 761 21 137 74 659 10.6 5.9 4.7 −53 522
001002 51 474 45 915 44 223 1 389 1 847 −458 3 236 5.7 7.6 −1.9 −3 694
001003 58 155 52 621 50 577 1 088 1 388 −300 2 476 3.9 5.0 −1.1 −2 776
001004 16 997 15 218 14 634 507 334 173 841 6.3 4.1 2.1 −668
001005 129 859 117 323 107 678 8 506 2 964 5 542 11 470 13.8 4.8 9.0 −5 928
001006 47 557 42 949 40 671 2 030 1 469 561 3 499 9.0 6.5 2.5 −2 938
001007 57 269 50 985 49 027 1 532 1 808 −276 3 340 5.7 6.7 −1.0 −3 616
001008 9 552 8 465 8 174 214 459 −245 673 4.8 10.2 −5.4 −918
001009 22 461 20 180 19 446 620 780 −160 1 400 5.8 7.3 −1.5 −1 560
001010 20 382 18 118 17 365 546 1 047 −501 1 593 5.7 10.9 −5.2 −2 094
001011 61 986 54 715 46 088 8 168 1 842 6 326 10 010 28.0 6.3 21.7 −3 684
002001 440 624 408 720 376 054 24 607 15 129 9 478 39 736 11.6 7.1 4.5 −30 258
002002 1 042 395 974 962 929 519 31 520 20 829 10 691 52 349 6.2 4.1 2.1 −41 658
002003 102 896 94 486 83 911 8 438 5 569 2 869 14 007 17.1 11.3 5.8 −11 138
002004 1 910 568 1 763 745 1 546 345 159 071 59 011 100 060 218 082 17.3 6.4 10.9 −118 022
002005 126 264 118 173 101 415 10 279 6 342 3 937 16 621 16.8 10.4 6.4 −12 684
002006 117 050 104 805 91 557 12 094 0 12 094 12 094 21.8 0.0 21.8 0
003001 72 358 67 109 64 658 2 192 3 939 −1 747 6 131 6.3 11.3 −5.0 −7 878
003002 62 884 57 738 54 515 2 759 5 421 −2 662 8 180 9.1 18.0 −8.8 −10 842
003003 290 063 270 823 249 997 17 969 11 442 6 527 29 411 12.8 8.2 4.7 −22 884
Fuente: Estimaciones del CONAPO.

Migración Intramunicipal

Matrices

### Municipios que no pertenecen en la muestra
municipios_nomuestra <- c("004012", "007125", "029048")

#Clave de los municipios 2020 
municipios <- MUN %>%
               select(CVE_MUN) %>%
                unique() %>%
                 filter(CVE_MUN %nin% municipios_nomuestra) %>%
                  pull(CVE_MUN)

Pob.5ymas <- mydata %>%
              mutate(CVE_MUN_RES = paste0(ENT_PAIS_RES_5A, MUN_RES_5A)) %>%
               mutate(ENT_PAIS_RES_5A = case_when(.$ENT_PAIS_RES_5A %in% estados ~.$ENT_PAIS_RES_5A,
                                                  .$ENT_PAIS_RES_5A %nin% estados ~ "888", #Residencia en otro país
                                                  .$ENT_PAIS_RES_5A %in% "997" ~ "997",
                                                  .$ENT_PAIS_RES_5A %in% "998" ~ "998",
                                                  .$ENT_PAIS_RES_5A %in% "997" ~ "999"),
                      CVE_MUN_RES = case_when(.$CVE_MUN_RES %in% municipios ~.$CVE_MUN_RES,
                                              #### Se excluyen los municipios que no fueron muestreados
                                              .$CVE_MUN_RES %in% municipios_nomuestra ~ "777",
                                               nchar(.$CVE_MUN_RES) == 3 ~ "888",  #Residencia en otro país
                                              .$CVE_MUN_RES %in% "997999" ~ '997999',
                                              .$ENT_PAIS_RES_5A %in% "997" ~ "997",
                                              .$ENT_PAIS_RES_5A %in% "998" ~ "998",
                                              .$ENT_PAIS_RES_5A %in% "999" ~ "999",
                                              .$MUN_RES_5A %in% "999" ~ "999")) %>%
                 mutate(I_Migracion = case_when(.$CVE_ENT == .$ENT_PAIS_RES_5A & .$ENT_PAIS_RES_5A %in% estados ~ 1,
                                                .$CVE_ENT != .$ENT_PAIS_RES_5A & .$ENT_PAIS_RES_5A %in% estados ~ 2,
                                                .$ENT_PAIS_RES_5A %nin% estados ~ 3)) %>%
                  mutate(EDAD = as.numeric(.$EDAD)) %>%
                   subset(EDAD >= 5 & EDAD <= 130) %>%
                    select(FACTOR, ESTRATO, UPM, CVE_MUN, CVE_MUN_RES, EDAD, I_Migracion) %>%
                     filter(CVE_MUN_RES %in% municipios & I_Migracion == 1) 

MC <- Pob.5ymas %>%
       svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)

saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/MC_intramunicipal.RDS"))
MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/MC_intramunicipal.RDS"))

Migrantes <- svytable(~CVE_MUN_RES + CVE_MUN, design = MC) 

Se genera la matriz cuadrada y se le asignan las etiquetas de municipios.

Migrantes <- Migrantes %>%
              as.data.frame() %>%
               expss::cross_cases(CVE_MUN, CVE_MUN_RES, weight = Freq) %>%
                as.data.frame() %>%
                 rename("CVE_MUN" = "row_labels") %>% 
                  arrange(CVE_MUN) %>%
                   slice(-1) 
            
rownames <- Migrantes %>% 
             mutate(CVE_MUN = substr(.$CVE_MUN, 9, 16)) %>% 
              pull(CVE_MUN)

colnames <- names(Migrantes) %>% 
             as.data.frame() %>% 
              slice(-1) %>% 
               rename("CVE_MUN" = ".") %>%
                mutate(`CVE_MUN` = substr(.$CVE_MUN, 13, 20)) %>%
                 pull(CVE_MUN)

# Se elimina la variable CVE_MUN
Migrantes <- Migrantes %>%
              select(-CVE_MUN)

rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames

saveRDS(Migrantes, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intramunicipal 2020.RDS"))
save(Migrantes, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))

require(openxlsx)
wb <- createWorkbook()
addWorksheet(wb, "M.Intramunicipal")
writeData(wb, 1, Migrantes %>% as.data.frame() %>% tibble::rownames_to_column(var = "CVE_MUN"), colNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intramunicipal 2020.xlsx"), overwrite = TRUE)

Matriz de migración reciente hace 5 años a nivel municipal, 2015 - 2020

Matriz de migración reciente a nivel municipal
Nivel intramunicipal
CVE_MUN 001001 001002 001003 001004 001005 001006 001007 001008 001009 001010 001011 002001 002002 002003 002004 002005 002006 003001 003002 003003 003008 003009 004001 004002 004003 004004 004005 004006 004007
001001 811844 835 491 0 1335 440 550 177 177 571 655 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001002 330 44223 10 5 28 15 33 0 72 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001003 139 0 50577 0 20 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001004 75 3 0 14634 16 3 80 0 18 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001005 3166 199 19 34 107678 98 46 30 89 46 201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001006 199 5 36 5 177 40671 234 2 50 0 358 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001007 238 13 25 135 33 93 49027 26 82 0 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001008 41 2 2 0 20 28 12 8174 35 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001009 75 31 0 15 0 73 153 0 19446 0 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001010 219 18 0 0 5 0 2 0 0 17365 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001011 2240 203 160 2 536 523 21 92 67 5 46088 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
002001 0 0 0 0 0 0 0 0 0 0 0 376054 1737 214 2213 115 0 0 0 0 0 0 0 0 0 0 0 0 0
002002 0 0 0 0 0 0 0 0 0 0 0 1139 929519 755 1677 347 0 0 0 0 0 0 0 0 0 0 0 0 0
002003 0 0 0 0 0 0 0 0 0 0 0 52 865 83911 1214 49 0 0 0 0 0 0 0 0 0 0 0 0 0
002004 0 0 0 0 0 0 0 0 0 0 0 1433 2600 1716 1546345 2894 0 0 0 0 0 0 0 0 0 0 0 0 0
002005 0 0 0 0 0 0 0 0 0 0 0 176 307 149 1299 101415 0 0 0 0 0 0 0 0 0 0 0 0 0
002006 0 0 0 0 0 0 0 0 0 0 0 0 82 0 267 0 91557 0 0 0 0 0 0 0 0 0 0 0 0
003001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64658 234 496 297 137 0 0 0 0 0 0 0
003002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 135 54515 173 43 22 0 0 0 0 0 0 0
003003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1741 1082 249997 2700 348 0 0 0 0 0 0 0
003008 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 237 287 981 260221 185 0 0 0 0 0 0 0
003009 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 98 26 169 12 15609 0 0 0 0 0 0 0
004001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52125 284 96 137 122 83 0
004002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 182 257197 1080 736 120 494 54
004003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 81 525 210329 452 0 0 33
004004 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 217 469 67521 0 0 0
004005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 139 9 56 28556 26 0
004006 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 92 0 7 5 36662 0
004007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 33 2 0 0 7728
004008 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 139 12 2 22 5 2
Fuente: Estimaciones del CONAPO.

Gráficos

ChordDiagram

Gráficos por estados

Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.

# Matriz cuadrada a nivel municipal 
load(paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))

rownames <- rownames(Migrantes) %>% 
             as.data.frame() %>%
              rename("CVE_MUN" = ".") %>%
               left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
                mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(CVE_MUN)

colnames <- colnames(Migrantes) %>% 
             as.data.frame() %>%
              rename("CVE_MUN" = ".") %>%
               left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
                mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(CVE_MUN)

rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames

# Nombre de los estados 
estado <- stringr::str_wrap(nom_estados, 100)

# Clave de los municipios 
### Municipios que no pertenecen en la muestra
municipios_nomuestra <- c("004012", "007125", "029048")

municipios <- MUN %>% 
               filter(CVE_MUN %nin% municipios_nomuestra) %>%
                mutate(municipios = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(municipios)

################################################################################
################################## Filtro ######################################
Inmigrantes <- Inmigrantes_function(municipios, Migrantes)

Emigrantes <- Emigrantes_function(municipios, Migrantes)

### Sacar el promedio de los flujos migratiorios para determinar como se van a grupar los estados 
#### Es importante correr la tabla1[[x]] sin filtros para determinar el número promedio de flujos de migración
#### Filtro <<<<< filter(value > 0 & rn != estado[x]) %>%
#p <- data.frame(estados = est,
 #               filtro_municipio = tabla_municipios)
#write.table(p, file = paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel intramunicipal.txt"), col.names = TRUE)
#write.xlsx(p, file = paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel intramunicipal.xlsx"), overwrite = TRUE)

#### Filtro de municipios
filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel intramunicipal.xlsx"), colNames = TRUE) %>%
               pull(filtro_municipio)

################################################################################
## Se generan los filtros correspondientes a la matriz cuadrada por estados 
tabla <- Migrantes %>%
          as.data.frame() %>%
           tibble::rownames_to_column(var = "rn") %>%
            melt(., id.vars = "rn", variable.name = "cn") %>%
             mutate_if(is.factor, as.character) %>%
              filter(value > 0)

tabla1 <- migration_flows_municipality(tabla = tabla, 
                                       filtro_mun = filtro,
                                       filtro_est = NULL,
                                       filtro_mig = filtro_mig, 
                                       filtro_out = NULL,
                                       category_group = estados, 
                                       category_names = nom_estados,
                                       group_mun = "Otros municipios",
                                       group_est = NULL)

################################################################################
## Se generan los filtros correspondientes a la matriz cuadrada por estados 
## Se sacan los flujos migratorios que pertencen a otros municipios
#tabla_municipios <- sapply(1:32, function(i){
#                                  tabla1[[i]] %>%
#                                   as.data.frame() %>%
#                                    adorn_totals(c("row", "col"), 
#                                                  fill = "-", 
#                                                   na.rm = TRUE, 
#                                                    ,,,,contains(colnames(tabla1[[i]]))) %>% 
#                                    select(Total) %>%
#                                     slice(nrow(.)) %>%
#                                      mutate(Total = .$Total/100) %>%
#                                       pull(Total)
#}
#)

## Se guardan las matrices de migración reciente para analizarlos después. 
wb <- createWorkbook()
for(i in 1:32){
     tabla <- tabla1[[i]] %>%
                as.data.frame() %>%
                 adorn_totals(c("row", "col"), 
                               fill = "-", 
                                na.rm = TRUE, 
          ,,,,contains(colnames(tabla1[[i]])))
                 
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz MR5a nivel intramunicipal_Reduccion.xlsx"), 
               overwrite = TRUE)
}
 
saveRDS(tabla1, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel intramunicipal.RDS"))
tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel intramunicipal.RDS"))

total_tablas <- totales(tabla1 = tabla1, 
                        Clave = "CVE_MUN", 
                        Inmigrantes = "Inmigrantes",  
                        Emigrantes = "Emigrantes")

porcentajes_tablas <- porcentajes(tabla1 = tabla1, 
                                  Clave = "CVE_MUN", 
                                  Inmigrantes = "%Inmigrantes",  
                                  Emigrantes = "%Emigrantes")

# Se guardan los totales de las matrices reducidas 
wb <- createWorkbook()
for(i in 1:32){
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, totales_tablas[[i]], colNames = TRUE, startCol = 1)
     writeData(wb, i, porcentajes_tablas[[i]], colNames = TRUE, startCol = 5)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz MR5a nivel intramunicipal_Reduccion_Totales.xlsx"), 
               overwrite = TRUE)
}

Dada la magnitud de municipios en algunos estados se seleccionan solo algunos de ellos.

tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel intramunicipal.RDS"))

## Genera una lista de vectores con los colores a nivel municipal 
#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")

tabla2 <- color_chord_diagram(tabla1 = tabla1, paleta)
file = "/Graficos/Municipio/01_Migracion reciente/2020/ChordDiagram de MR5a desagregado a nivel intramunicipal.pdf"

## Gráficos a nivel estatal 
chord_diagram_graph(file = file, 
                    width = 15, 
                    height = 10, 
                    family = "Montserrat Medium", 
                    paleta = paleta, 
                    tabla1 = tabla1, 
                    tabla2 = tabla2, 
                    color_labels = "#000C7D",
                    transparency = 0,
                    circo.text = 9,
                    circos.axis.text = 6,
                    adj.text =c(-0.05, 0.5), #Ajuste de las etiquetas (x, y)
                    adj.ylim = 0.1,
                    gap.degree = 2, 
                    clock.wise = FALSE,
                    track.margin = c(-0.07, 0.1),
                    margin = rep(0, 4))

Etiquetas

file = "/Graficos/Municipio/01_Migracion reciente/2020/Etiquetas a nivel intramunicipal.pdf"

labels_chord_diagram(file = file, 
                     width = 7, 
                     height = 8, 
                     family = "Montserrat Medium", 
                     paleta = paleta, 
                     tabla1 = tabla1, 
                     labels = nom_estados)

Gráfico Sankey

# Matriz cuadrada a nivel municipal 
load(paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))

tabla <- Migrantes %>%
          as.data.frame() %>%
           tibble::rownames_to_column(var = "rn") %>%
            melt(., id.vars = "rn", variable.name = "cn") %>%
             mutate_if(is.factor, as.character)

################################################################################
################################## Filtro ######################################
Inmigrantes  <- Migrantes %>%
                 as.data.frame() %>%
                  tibble::rownames_to_column(var = "rn") %>% 
                   melt(., id.vars = "rn", variable.name = "cn") %>%
                    mutate_if(is.factor, as.character) %>%
                     mutate(value = ifelse((.$rn != .$cn) & (substr(.$rn, 1, 3) %in% estados | substr(.$cn, 1, 3) %in% estados), value, 0)) %>%
                      filter(value > 0) %>%
                       group_by(rn) %>% 
                        summarise(Inmigrantes = sum(value, na.rm = TRUE)) 

Emigrantes <- Migrantes %>%
               as.data.frame() %>%
                tibble::rownames_to_column(var = "rn") %>% 
                 melt(., id.vars = "rn", variable.name = "cn") %>%
                  mutate_if(is.factor, as.character) %>%
                   mutate(value = ifelse((.$rn != .$cn) & (substr(.$rn, 1, 3) %in% estados | substr(.$cn, 1, 3) %in% estados), value, 0)) %>%
                    filter(value > 0) %>%
                     group_by(cn) %>% 
                      summarise(Emigrantes = sum(value, na.rm = TRUE))     

################################## Filtro ######################################
filtro  <- Inmigrantes %>%
            full_join(., Emigrantes, by = c("rn" = "cn")) %>%
             mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
              filter(value < 5000) %>% 
               pull(rn)
################################################################################

tabla1 <- lapply(1:32, function(x){
                   tabla %>%
                    mutate(rn = case_when(substr(.$rn, 1, 3) %in% estados[x] & .$rn %in% filtro ~ paste0("Otros municipios (", estados[x], ")"),
                                          substr(.$rn, 1, 3) %in% estados[x]  & .$rn %nin% filtro ~ .$rn,
                                          substr(.$rn, 1, 3) %nin% estados[x] ~ substr(.$rn, 1, 3)),
                           cn = case_when(substr(.$cn, 1, 3) %in% estados[x] & .$cn %in% filtro ~ paste0("Otros municipios (", estados[x], ")"),
                                          substr(.$cn, 1, 3) %in% estados[x] & .$cn %nin% filtro ~ .$cn,
                                          substr(.$cn, 1, 3) %nin% estados[x] ~ substr(.$cn, 1, 3))) %>%

                     mutate(value = ifelse(.$rn != .$cn, .$value, 0)) %>%
                      filter(value > 0) 
  }
)
p <- lapply(1:32, function(x){
             tabla1[[x]] %>% 
              ggplot(aes(axis1 = rn, 
                          axis2 = cn, 
                           y = value),  # c("value", "freq", "tasa")
                      reverse = FALSE, 
                       na.rm = TRUE) +
               geom_alluvium(aes(fill = rn),
                              curve_type = "quintic", 
                               color = "transparent", 
                                alpha = 0.85, 
                                 lwd = 0.001, 
                                  width = 1/5,
                                   reverse = FALSE) +
                 geom_stratum(aes(fill = cn), 
                               color = "white", 
                                alpha = 0.65,  
                                 lwd = 0.001, 
                                  width = 1/5,
                                   reverse = FALSE) +
                  geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""), 
                                       fontface =  ifelse(after_stat(x) == 1, 'bold', 'plain')),
                                   stat = "stratum", 
                                    size = 3, 
                                     direction = "y", 
                                      nudge_x = -.2,
                                       min.segment.length = unit(1, "lines"),
                                        force = 1,
                                         force_pull = 0,
                                          family = "montserrat",
                                           reverse = FALSE) +
                   geom_text_repel(aes(label = ifelse(after_stat(x)  == 2, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""),
                                        fontface =  ifelse(after_stat(x) == 2, 'bold', 'plain')),
                                    stat = "stratum", 
                                     size = 3,
                                      direction = "y", 
                                       nudge_x = .2, 
                                        force = 1,
                                         force_pull = 0,
                                          family = "montserrat",
                                           reverse = FALSE) +
                     theme_void() + 
                      theme(plot.margin = margin(t = 1, r = 5, b = 1, l = 0, "cm"),
                             text = element_text(family = "montserrat"),
                              axis.text = element_blank(),
                               axis.title = element_blank(),
                                strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
                                 legend.key.size = unit(0.5, "cm"),
                                  legend.text = element_text(size = 7, family = "montserrat"),
                                   legend.position = c(1.02, .5)) + 
                       scale_x_discrete(expand = c(-0.1, 0.5)) +
                        scale_fill_viridis_d(option = "A", end = 1, begin = 0.2) +
                         guides(fill = guide_legend(ncol = 1, na.translate = F)) + 
                          labs(fill = "", 
                               color = "")
  }
)

path = paste0(here::here(), "/Graficos/Municipio/01_Migracion reciente/2020/GSankey de MR5a desagregado a nivel intramunicipal.pdf")
ggexport(list = p, width = 18, height = 10, dpi = 400, filename = path)

Indicadores

Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora

################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
              as.data.frame() %>%
               group_by(CVE_MUN) %>%
                summarise(Pob.Total = sum(FACTOR)) 

################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
              as.data.frame() %>%
               mutate(EDAD = as.numeric(.$EDAD)) %>%
                subset(EDAD >= 5 & EDAD <=130) %>%
                 group_by(CVE_MUN) %>%
                  summarise(Pob.5ymas = sum(FACTOR)) 

################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))

Residentes <- Migrantes %>%
               rownames_to_column() %>%
                gather(CVE_MUN, Value, -rowname)%>%
                 filter(rowname == CVE_MUN) %>%
                  select(-rowname) %>%
                   droplevels() %>%
                    rename("Residentes" = "Value") 

################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- Migrantes %>% 
                as.data.frame() %>%
                 tibble::rownames_to_column(var = "CVE_MUN") %>%
                  melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
                   mutate_at(vars(3), as.numeric) %>%
                    as_tibble() %>%
                     filter(CVE_MUN != CVE_MUN_RES) %>%
                      group_by(CVE_MUN) %>%
                       summarise(Inmigrantes = sum(value, na.rm = TRUE))

################################################################################
############################### Emigrantes #####################################
Emigrantes <- Migrantes %>% 
               as.data.frame() %>%
                tibble::rownames_to_column(var = "CVE_MUN") %>%
                 melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
                  mutate_at(vars(3), as.numeric) %>%
                   as_tibble() %>%
                    filter(CVE_MUN != CVE_MUN_RES) %>%
                     group_by(CVE_MUN_RES) %>%
                      summarise(Emigrantes = sum(value, na.rm = TRUE)) %>%
                       rename("CVE_MUN" = "CVE_MUN_RES") 

tabla <- Pob.Total %>%
          left_join(., Pob.5ymas, by = c("CVE_MUN")) %>%
          left_join(., Residentes, by = c("CVE_MUN")) %>%
          left_join(., Inmigrantes, by = c("CVE_MUN")) %>%
          left_join(., Emigrantes, by = c("CVE_MUN")) %>%
           mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
                  Mig.Bruta = .$Inmigrantes + .$Emigrantes, 
                  Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
                  Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
                  Tasa.Mig = Tasa.Inmig - Tasa.Emig, 
                  Eficacia = Mig.Neta - Mig.Bruta)

write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Indicadores de MR5a a nivel intramunicipal 2020.xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Indicadores de MR5a a nivel intramunicipal 2020.RData"))
Indicadores de migración reciente
Nivel intramunicipal
CVE_MUN 001001 001002 001003 001004 001005 001006 001007 001008 001009 001010 001011 002001 002002 002003 002004 002005 002006 003001 003002 003003 003008 003009 004001 004002 004003 004004 004005 004006 004007 004008 004009 004010 004011 005001 005002 005003 005004 005005 005006 005007 005008 005009 005010 005011 005012 005013 005014 005015 005016 005017 005018 005019 005020 005021 005022 005023 005024 005025 005026 005027 005028 005029 005030 005031 005032 005033 005034 005035 005036 005037 005038 006001 006002 006003 006004 006005 006006 006007 006008 006009 006010 007001 007002 007003 007004 007005 007006 007007 007008 007009 007010 007011 007012 007013 007014 007015 007016 007017 007018 007019 007020 007021 007022 007023 007024 007025 007026 007027 007028 007029 007030 007031 007032 007033 007034 007035 007036 007037 007038 007039 007040 007041 007042 007043 007044 007045 007046 007047 007048 007049 007050 007051 007052 007053 007054 007055 007056 007057 007058 007059 007060 007061 007062 007063 007064 007065 007066 007067 007068 007069 007070 007071 007072 007073 007074 007075 007076 007077 007078 007079 007080 007081 007082 007083 007084 007085 007086 007087 007088 007089 007090 007091 007092 007093 007094 007096 007097 007098 007099 007100 007101 007102 007103 007104 007105 007106 007107 007108 007109 007110 007111 007112 007113 007114 007115 007116 007117 007118 007119 007120 007121 007122 007123 007124 008001 008002 008003 008004 008005 008006 008007 008008 008009 008010 008011 008012 008013 008014 008015 008016 008017 008018 008019 008020 008021 008022 008023 008024 008025 008026 008027 008028 008029 008030 008031 008032 008033 008034 008035 008036 008037 008038 008039 008040 008041 008042 008043 008044 008045 008046 008047 008048 008049 008050 008051 008052 008053 008054 008055 008056 008057 008058 008059 008060 008061 008062 008063 008064 008065 008066 008067 009002 009003 009004 009005 009006 009007 009008 009009 009010 009011 009012 009013 009014 009015 009016 009017 010001 010002 010003 010004 010005 010006 010007 010008 010009 010010 010011 010012 010013 010014 010015 010016 010017 010018 010019 010020 010021 010022 010023 010024 010025 010026 010027 010028 010029 010030 010031 010032 010033 010034 010035 010036 010037 010038 010039 011001 011002 011003 011004 011005 011006 011007 011008 011009 011010 011011 011012 011013 011014 011015 011016 011017 011018 011019 011020 011021 011022 011023 011024 011025 011026 011027 011028 011029 011030 011031 011032 011033 011034 011035 011036 011037 011038 011039 011040 011041 011042 011043 011044 011045 011046 012001 012002 012003 012004 012005 012006 012007 012008 012009 012010 012011 012012 012013 012014 012015 012016 012017 012018 012019 012020 012021 012022 012023 012024 012025 012026 012027 012028 012029 012030 012031 012032 012033 012034 012035 012036 012037 012038 012039 012040 012041 012042 012043 012044 012045 012046 012047 012048 012049 012050 012051 012052 012053 012054 012055 012056 012057 012058 012059 012060 012061 012062 012063 012064 012065 012066 012067 012068 012069 012070 012071 012072 012073 012074 012075 012076 012077 012078 012079 012080 012081 013001 013002 013003 013004 013005 013006 013007 013008 013009 013010 013011 013012 013013 013014 013015 013016 013017 013018 013019 013020 013021 013022 013023 013024 013025 013026 013027 013028 013029 013030 013031 013032 013033 013034 013035 013036 013037 013038 013039 013040 013041 013042 013043 013044 013045 013046 013047 013048 013049 013050 013051 013052 013053 013054 013055 013056 013057 013058 013059 013060 013061 013062 013063 013064 013065 013066 013067 013068 013069 013070 013071 013072 013073 013074 013075 013076 013077 013078 013079 013080 013081 013082 013083 013084 014001 014002 014003 014004 014005 014006 014007 014008 014009 014010 014011 014012 014013 014014 014015 014016 014017 014018 014019 014020 014021 014022 014023 014024 014025 014026 014027 014028 014029 014030 014031 014032 014033 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Fuente: Estimaciones del CONAPO.

Migración Intermunicipal

Matrices

### Municipios que no pertenecen en la muestra
municipios_nomuestra <- c("004012", "007125", "029048")

#Clave de los municipios 2020 
municipios <- MUN %>%
               select(CVE_MUN) %>%
                unique() %>%
                 filter(CVE_MUN %nin% municipios_nomuestra) %>%
                  pull(CVE_MUN)

Pob.5ymas <- mydata %>%
              mutate(CVE_MUN_RES = paste0(ENT_PAIS_RES_5A, MUN_RES_5A)) %>%
               mutate(ENT_PAIS_RES_5A = case_when(.$ENT_PAIS_RES_5A %in% estados ~.$ENT_PAIS_RES_5A,
                                                  .$ENT_PAIS_RES_5A %nin% estados ~ "888", #Residencia en otro país
                                                  .$ENT_PAIS_RES_5A %in% "997" ~ "997",
                                                  .$ENT_PAIS_RES_5A %in% "998" ~ "998",
                                                  .$ENT_PAIS_RES_5A %in% "997" ~ "999"),
                      CVE_MUN_RES = case_when(.$CVE_MUN_RES %in% municipios ~.$CVE_MUN_RES,
                                              #### Se excluyen los municipios que no fueron muestreados
                                              .$CVE_MUN_RES %in% municipios_nomuestra ~ "777",
                                               nchar(.$CVE_MUN_RES) == 3 ~ "888",  #Residencia en otro país
                                              .$CVE_MUN_RES %in% "997999" ~ '997999',
                                              .$ENT_PAIS_RES_5A %in% "997" ~ "997",
                                              .$ENT_PAIS_RES_5A %in% "998" ~ "998",
                                              .$ENT_PAIS_RES_5A %in% "999" ~ "999",
                                              .$MUN_RES_5A %in% "999" ~ "999")) %>%
                 mutate(I_Migracion = case_when(.$CVE_ENT == .$ENT_PAIS_RES_5A & .$ENT_PAIS_RES_5A %in% estados ~ 1,
                                                .$CVE_ENT != .$ENT_PAIS_RES_5A & .$ENT_PAIS_RES_5A %in% estados ~ 2,
                                                .$ENT_PAIS_RES_5A %nin% estados ~ 3)) %>%
                  mutate(EDAD = as.numeric(.$EDAD)) %>%
                   subset(EDAD >= 5 & EDAD <= 130) %>%
                    select(FACTOR, ESTRATO, UPM, CVE_MUN, CVE_MUN_RES, EDAD, I_Migracion) %>%
                     filter(CVE_MUN_RES %in% municipios & I_Migracion == 2) 

MC <- Pob.5ymas %>%
       svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)

saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/MC_intermunicipal.RDS"))
MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/MC_intermunicipal.RDS"))

Migrantes <- svytable(~CVE_MUN_RES + CVE_MUN, design = MC) 

Se genera la matriz cuadrada y se le asignan las etiquetas de municipios.

Migrantes <- Migrantes %>%
              as.data.frame() %>%
               expss::cross_cases(CVE_MUN, CVE_MUN_RES, weight = Freq) %>%
                as.data.frame() %>%
                 rename("CVE_MUN" = "row_labels") %>% 
                  arrange(CVE_MUN) %>%
                   slice(-1) 
            
rownames <- Migrantes %>% 
             mutate(CVE_MUN = substr(.$CVE_MUN, 9, 16)) %>% 
              pull(CVE_MUN)

colnames <- names(Migrantes) %>% 
             as.data.frame() %>% 
              slice(-1) %>% 
               rename("CVE_MUN" = ".") %>%
                mutate(`CVE_MUN` = substr(.$CVE_MUN, 13, 20)) %>%
                 pull(CVE_MUN)

# Se elimina la variable CVE_MUN
Migrantes <- Migrantes %>%
              select(-CVE_MUN)

rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames

saveRDS(Migrantes, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intermunicipal 2020.RDS"))
save(Migrantes, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))

require(openxlsx)
wb <- createWorkbook()
addWorksheet(wb, "M.Intermunicipal")
writeData(wb, 1, Migrantes %>% as.data.frame() %>% tibble::rownames_to_column(var = "CVE_MUN"), colNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intermunicipal 2020.xlsx"), overwrite = TRUE)

Matriz de migración reciente hace 5 años a nivel municipal, 2015 - 2020

Matriz de migración reciente a nivel municipal
Nivel intermunicipal
CVE_MUN 001001 001002 001003 001004 001005 001006 001007 001008 001009 001010 001011 002001 002002 002003 002004 002005 003001 003002 003003 003008 003009 004001 004002 004003 004004 004005 004006 004007 004008
001001 0 0 0 0 0 0 0 0 0 0 0 183 426 0 120 59 0 0 177 0 0 0 0 0 0 0 0 0 0
001002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001003 0 0 0 0 0 0 0 0 0 0 0 4 6 6 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0
001004 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0
001005 0 0 0 0 0 0 0 0 0 0 0 0 9 0 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001006 0 0 0 0 0 0 0 0 0 0 0 0 4 0 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001008 0 0 0 0 0 0 0 0 0 0 0 4 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001009 0 0 0 0 0 0 0 0 0 0 0 0 8 3 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001010 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
001011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 156 0 0 0 0 0 0 0 0 0 0 0 0 0
002001 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 449 1032 701 245 56 0 0 0 0 0 0 0 0
002002 174 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 79 150 0 0 0 0 0 0 0 0 0
002003 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 97 0 0 0 0 0 0 0 0 0
002004 165 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 648 965 913 0 0 165 0 0 0 0 0 0
002005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65 25 0 0 24 37 0 0 0 0 0
002006 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 460 70 150 66 0 0 0 0 0 0 0 0
003001 0 0 0 0 0 0 0 0 0 0 0 39 59 0 28 0 0 0 0 0 0 0 8 0 0 0 0 0 0
003002 0 0 0 0 0 0 0 0 0 0 0 786 225 11 119 18 0 0 0 0 0 0 0 0 0 0 0 0 0
003003 20 0 0 0 0 0 0 0 0 0 0 494 225 0 643 0 0 0 0 0 0 0 0 0 0 0 0 0 0
003008 119 0 0 0 0 0 0 0 0 0 0 272 174 26 426 221 0 0 0 0 0 0 76 0 28 0 0 0 0
003009 0 0 0 0 0 0 0 0 0 0 0 26 12 0 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0
004001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
004002 17 0 0 0 0 0 0 0 0 0 0 40 0 0 51 0 0 49 0 0 0 0 0 0 0 0 0 0 0
004003 32 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
004004 0 0 0 0 0 0 0 0 0 0 0 36 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
004005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
004006 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
004007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
004008 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fuente: Estimaciones del CONAPO.

Gráficos

ChordDiagram

Gráficos por estados

Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.

# Matriz cuadrada a nivel municipal 
load(paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))

rownames <- rownames(Migrantes) %>% 
             as.data.frame() %>%
              rename("CVE_MUN" = ".") %>%
               left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
                mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(CVE_MUN)

colnames <- colnames(Migrantes) %>% 
             as.data.frame() %>%
              rename("CVE_MUN" = ".") %>%
               left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
                mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(CVE_MUN)

rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames

# Nombre de los estados 
estado <- stringr::str_wrap(nom_estados, 100)

# Clave de los municipios 
### Municipios que no pertenecen en la muestra
municipios_nomuestra <- c("004012", "007125", "029048")

municipios <- MUN %>% 
               filter(CVE_MUN %nin% municipios_nomuestra) %>%
                mutate(municipios = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
                 pull(municipios)
             
################################################################################
################################## Filtro ######################################
Inmigrantes <- Inmigrantes_function(municipios, Migrantes)

Emigrantes <- Emigrantes_function(municipios, Migrantes)

################################## Filtro ######################################
filtro  <- Inmigrantes %>%
            full_join(., Emigrantes, by = c("rn" = "cn")) %>%
             mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
              filter(value > 0)

filtro_est <- Migrantes %>%
               as.data.frame() %>%
                tibble::rownames_to_column(var = "rn") %>% 
                 melt(., id.vars = "rn", variable.name = "cn") %>%
                  mutate_if(is.factor, as.character) %>%
                   filter(value > 0)

### Sacar el promedio de los flujos migratiorios para determinar como se van a grupar los estados 
#### Es importante correr la tabla1[[x]] sin filtros para determinar el número promedio de flujos de migración
#### Filtro <<<<< filter(value > 0 & rn != estado[x]) %>%
#p <- data.frame(estados = est,
 #               filtro_municipio = tabla_municipios,
  #              filtro_estado = tabla_estados)
#write.xlsx(p, file = paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel intermunicipal.xlsx"), overwrite = TRUE)

#### Filtro de municipios
filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel intermunicipal.xlsx"), colNames = TRUE) %>%
               pull(filtro_municipio)

#### Filtro de estados 
filtro_out <- read.xlsx(paste0(here::here(), "/Bases/Municipio/01_Migracion reciente/2020/Filtro a nivel intermunicipal.xlsx"), colNames = TRUE) %>%
               pull(filtro_estado)

################################################################################
## Se generan los filtros correspondientes a la matriz cuadrada por estados 
tabla <- Migrantes %>%
          as.data.frame() %>%
           tibble::rownames_to_column(var = "rn") %>%
            melt(., id.vars = "rn", variable.name = "cn") %>%
             mutate_if(is.factor, as.character) %>%
              filter(value > 0)

tabla1 <- migration_flows_municipality(tabla = tabla, 
                                       filtro_mun = filtro,
                                       filtro_est = filtro_est,
                                       filtro_mig = filtro_mig, 
                                       filtro_out = filtro_out, 
                                       category_group = estados, 
                                       category_names = nom_estados,
                                       group_mun = "Otros municipios",
                                       group_est = "Otros estados")

## Se sacan los flujos migratorios que pertencen a otros estados
#tabla_estados <- sapply(1:32, function(i){
#                                 tabla1[[i]] %>%
#                                  as.data.frame() %>%
#                                   adorn_totals(c("row", "col"), 
#                                                 fill = "-", 
#                                                  na.rm = TRUE, 
#                                                   ,,,,contains(colnames(tabla1[[i]]))) %>% 
#                                    select(`Otros estados`) %>%
#                                     slice(nrow(.)) %>%
#                                      mutate(`Otros estados` = .$`Otros estados`/10) %>%
#                                       pull(`Otros estados`)
#})

## Se sacan los flujos migratorios que pertencen a otros municipios
#tabla_municipios <- sapply(1:32, function(i){
#                               tabla1[[i]] %>%
#                                as.data.frame() %>%
#                                 select(-c(`Otros estados`)) %>%
#                                  adorn_totals(c("row", "col"), 
#                                                fill = "-", 
#                                                 na.rm = TRUE, 
#                                                  ,,,,contains(colnames(tabla1[[i]]))) %>% 
#                                    slice(nrow(.)) %>%
#                                     mutate(Total = .$Total/50) %>%
#                                      pull(Total)
#})

## Se guardan las matrices de migración reciente para analizarlos después. 
wb <- createWorkbook()
for(i in 1:32){
     tabla <- tabla1[[i]] %>%
                as.data.frame() %>%
                 adorn_totals(c("row", "col"), 
                               fill = "-", 
                                na.rm = TRUE, 
          ,,,,contains(colnames(tabla1[[i]])))
                 
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz MR5a a nivel intermunicipal_Reduccion.xlsx"), 
               overwrite = TRUE)
}

saveRDS(tabla1, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel intermunicipal.RDS"))
tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel intermunicipal.RDS"))

total_tablas <- totales(tabla1 = tabla1, 
                        Clave = "CVE_MUN", 
                        Inmigrantes = "Inmigrantes",  
                        Emigrantes = "Emigrantes")

porcentajes_tablas <- porcentajes(tabla1 = tabla1, 
                                  Clave = "CVE_MUN", 
                                  Inmigrantes = "%Inmigrantes",  
                                  Emigrantes = "%Emigrantes")

# Se guardan los totales de las matrices reducidas 
wb <- createWorkbook()
for(i in 1:32){
     addWorksheet(wb, paste(est[i]))
     writeData(wb, i, totales_tablas[[i]], colNames = TRUE, startCol = 1)
     writeData(wb, i, porcentajes_tablas[[i]], colNames = TRUE, startCol = 5)
     saveWorkbook(wb, 
                  file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz MR5a a nivel intermunicipal_Reduccion_Totales.xlsx"), 
               overwrite = TRUE)
}

Dada la magnitud de municipios en algunos estados se seleccionan solo algunos de ellos.

tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Tabla MR5a a nivel intermunicipal.RDS"))

## Genera una lista de vectores con los colores a nivel municipal 
#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")

tabla2 <- color_chord_diagram(tabla1 = tabla1, paleta)
file = "/Graficos/Municipio/01_Migracion reciente/2020/ChordDiagram de MR5a desagregado a nivel intermunicipal.pdf"

## Gráficos a nivel intermunicipal
chord_diagram_graph(file = file, 
                    width = 15, 
                    height = 10, 
                    family = "Montserrat Medium", 
                    paleta = paleta, 
                    tabla1 = tabla1, 
                    tabla2 = tabla2, 
                    color_labels = "#000C7D",
                    transparency = 0,
                    circo.text = 9,
                    circos.axis.text = 6,
                    adj.text =c(-0.05, 0.5), #Ajuste de las etiquetas (x, y)
                    adj.ylim = 0.1,
                    gap.degree = 2, 
                    clock.wise = FALSE,
                    track.margin = c(-0.07, 0.1),
                    margin = rep(0, 4))

Etiquetas

file = "/Graficos/Municipio/01_Migracion reciente/2020/Etiquetas a nivel intermunicipal.pdf"

labels_chord_diagram(file = file, 
                     width = 7, 
                     height = 8, 
                     family = "Montserrat Medium", 
                     paleta = paleta, 
                     tabla1 = tabla1, 
                     labels = nom_estados)

Gráfico Sankey

# Matriz cuadrada a nivel municipal 
load(paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))

tabla <- Migrantes %>%
          as.data.frame() %>%
           tibble::rownames_to_column(var = "rn") %>%
            melt(., id.vars = "rn", variable.name = "cn") %>%
             mutate_if(is.factor, as.character)

################################################################################
################################## Filtro ######################################
Inmigrantes  <- Migrantes %>%
                 as.data.frame() %>%
                  tibble::rownames_to_column(var = "rn") %>% 
                   melt(., id.vars = "rn", variable.name = "cn") %>%
                    mutate_if(is.factor, as.character) %>%
                     mutate(value = ifelse((.$rn != .$cn) & (substr(.$rn, 1, 3) %in% estados | substr(.$cn, 1, 3) %in% estados), value, 0)) %>%
                      filter(value > 0) %>%
                       group_by(rn) %>% 
                        summarise(Inmigrantes = sum(value, na.rm = TRUE)) 

Emigrantes <- Migrantes %>%
               as.data.frame() %>%
                tibble::rownames_to_column(var = "rn") %>% 
                 melt(., id.vars = "rn", variable.name = "cn") %>%
                  mutate_if(is.factor, as.character) %>%
                   mutate(value = ifelse((.$rn != .$cn) & (substr(.$rn, 1, 3) %in% estados | substr(.$cn, 1, 3) %in% estados), value, 0)) %>%
                    filter(value > 0) %>%
                     group_by(cn) %>% 
                      summarise(Emigrantes = sum(value, na.rm = TRUE))     

################################## Filtro ######################################
filtro  <- Inmigrantes %>%
            full_join(., Emigrantes, by = c("rn" = "cn")) %>%
             mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
              filter(value < 5000) %>% 
               pull(rn)
################################################################################

tabla1 <-  lapply(1:32, function(x){
                   tabla %>%
                    mutate(rn = case_when(substr(.$rn, 1, 3) %in% estados[x] & .$rn %in% filtro ~ paste0(estados[x], " Otros municipios (", estados[x], ")"),
                                          substr(.$rn, 1, 3) %in% estados[x]  & .$rn %nin% filtro ~ .$rn,
                                          substr(.$rn, 1, 3) %nin% estados[x] ~ paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))])),
                           cn = case_when(substr(.$cn, 1, 3) %in% estados[x] & .$cn %in% filtro ~ paste0(estados[x], " Otros municipios (", estados[x], ")"),
                                          substr(.$cn, 1, 3) %in% estados[x] & .$cn %nin% filtro ~ .$cn,
                                          substr(.$cn, 1, 3) %nin% estados[x] ~ paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]))) %>%
                     mutate(value = ifelse(.$rn != .$cn & (substr(.$rn, 1, 3) %in% estados[x] | substr(.$cn, 1, 3) %in% estados[x]), .$value, 0)) %>%
                      filter(value > 0)  
  }
)
p <- lapply(1:32, function(x){
             tabla1[[x]] %>% 
              ggplot(aes(axis1 = rn, 
                          axis2 = cn, 
                           y = value),  # c("value", "freq", "tasa")
                      reverse = FALSE, 
                       na.rm = TRUE) +
               geom_alluvium(aes(fill = rn),
                              curve_type = "quintic", 
                               color = "transparent", 
                                alpha = 0.85, 
                                 lwd = 0.001, 
                                  width = 1/5,
                                   reverse = FALSE) +
                 geom_stratum(aes(fill = cn), 
                               color = "white", 
                                alpha = 0.65,  
                                 lwd = 0.001, 
                                  width = 1/5,
                                   reverse = FALSE) +
                  geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""), 
                                       fontface =  ifelse(after_stat(x) == 1, 'bold', 'plain')),
                                   stat = "stratum", 
                                    size = 3, 
                                     direction = "y", 
                                      nudge_x = -.2,
                                       min.segment.length = unit(1, "lines"),
                                        force = 1,
                                         force_pull = 0,
                                          family = "montserrat",
                                           reverse = FALSE) +
                   geom_text_repel(aes(label = ifelse(after_stat(x)  == 2, paste0(as.character(after_stat(stratum)),  ": ", prettyNum(count, big.mark = " ")), ""),
                                        fontface =  ifelse(after_stat(x) == 2, 'bold', 'plain')),
                                    stat = "stratum", 
                                     size = 3,
                                      direction = "y", 
                                       nudge_x = .2, 
                                        force = 1,
                                         force_pull = 0,
                                          family = "montserrat",
                                           reverse = FALSE) +
                     theme_void() + 
                      theme(plot.margin = margin(t = 1, r = 5, b = 1, l = 0, "cm"),
                             text = element_text(family = "montserrat"),
                              axis.text = element_blank(),
                               axis.title = element_blank(),
                                strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
                                 legend.key.size = unit(0.5, "cm"),
                                  legend.text = element_text(size = 7, family = "montserrat"),
                                   legend.position = c(1.02, .5)) + 
                       scale_x_discrete(expand = c(-0.1, 0.5)) +
                        scale_fill_viridis_d(option = "A", end = 1, begin = 0.2) +
                         guides(fill = guide_legend(ncol = 1, na.translate = F)) + 
                          labs(fill = "", 
                               color = "")
  }
)

path = paste0(here::here(), "/Graficos/Municipio/01_Migracion reciente/2020/GSankey de MR5a desagregado a nivel intermunicipal.pdf")
ggexport(list = p, width = 18, height = 10, dpi = 400, filename = path)

Indicadores

Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora

################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
              as.data.frame() %>%
               group_by(CVE_MUN) %>%
                summarise(Pob.Total = sum(FACTOR)) 

################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
              as.data.frame() %>%
               mutate(EDAD = as.numeric(.$EDAD)) %>%
                subset(EDAD >= 5 & EDAD <=130) %>%
                 group_by(CVE_MUN) %>%
                  summarise(Pob.5ymas = sum(FACTOR)) 

################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))

Residentes <- Migrantes %>%
               rownames_to_column() %>%
                gather(CVE_MUN, Value, -rowname)%>%
                 filter(rowname == CVE_MUN) %>%
                  select(-rowname) %>%
                   droplevels() %>%
                    rename("Residentes" = "Value") 

################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- Migrantes %>% 
                as.data.frame() %>%
                 tibble::rownames_to_column(var = "CVE_MUN") %>%
                  melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
                   mutate_at(vars(3), as.numeric) %>%
                    as_tibble() %>%
                     filter(CVE_MUN != CVE_MUN_RES) %>%
                      group_by(CVE_MUN) %>%
                       summarise(Inmigrantes = sum(value, na.rm = TRUE))

################################################################################
############################### Emigrantes #####################################
Emigrantes <- Migrantes %>% 
               as.data.frame() %>%
                tibble::rownames_to_column(var = "CVE_MUN") %>%
                 melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
                  mutate_at(vars(3), as.numeric) %>%
                   as_tibble() %>%
                    filter(CVE_MUN != CVE_MUN_RES) %>%
                     group_by(CVE_MUN_RES) %>%
                      summarise(Emigrantes = sum(value, na.rm = TRUE)) %>%
                       rename("CVE_MUN" = "CVE_MUN_RES") 

tabla <- Pob.Total %>%
          left_join(., Pob.5ymas, by = c("CVE_MUN")) %>%
          left_join(., Residentes, by = c("CVE_MUN")) %>%
          left_join(., Inmigrantes, by = c("CVE_MUN")) %>%
          left_join(., Emigrantes, by = c("CVE_MUN")) %>%
           mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
                  Mig.Bruta = .$Inmigrantes + .$Emigrantes, 
                  Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
                  Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
                  Tasa.Mig = Tasa.Inmig - Tasa.Emig, 
                  Eficacia = Mig.Neta - Mig.Bruta)

write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Indicadores de MR5a a nivel intermunicipal 2020.xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/01_Migracion reciente/2020/Indicadores de MR5a a nivel intermunicipal 2020.RData"))
Indicadores de migración reciente
Nivel intermunicipal
Clave del municipio Pob.Total Pob.5ymas Residentes Inmigrantes Emigrantes Mig.Neta Mig.Bruta Tasa.Inmig Tasa.Emig Tasa.Mig Eficacia
001001 945 506 869 741 0 42 667 20 039 22 628 62 706 9.4 4.42 5.0 −40 078
001002 51 474 45 915 0 840 538 302 1 378 3.5 2.21 1.2 −1 076
001003 58 155 52 621 0 908 645 263 1 553 3.3 2.33 0.9 −1 290
001004 16 997 15 218 0 310 138 172 448 3.8 1.71 2.1 −276
001005 129 859 117 323 0 4 578 794 3 784 5 372 7.4 1.28 6.1 −1 588
001006 47 557 42 949 0 964 196 768 1 160 4.3 0.87 3.4 −392
001007 57 269 50 985 0 826 656 170 1 482 3.1 2.42 0.6 −1 312
001008 9 552 8 465 0 66 132 −66 198 1.5 2.93 −1.5 −264
001009 22 461 20 180 0 232 190 42 422 2.2 1.78 0.4 −380
001010 20 382 18 118 0 286 369 −83 655 3.0 3.83 −0.9 −738
001011 61 986 54 715 0 4 319 500 3 819 4 819 14.8 1.71 13.1 −1 000
002001 440 624 408 720 0 20 328 12 329 7 999 32 657 9.6 5.81 3.8 −24 658
002002 1 042 395 974 962 0 27 602 15 238 12 364 42 840 5.5 3.02 2.5 −30 476
002003 102 896 94 486 0 6 258 2 735 3 523 8 993 12.7 5.54 7.1 −5 470
002004 1 910 568 1 763 745 0 150 428 52 341 98 087 202 769 16.4 5.70 10.7 −104 682
002005 126 264 118 173 0 8 348 2 937 5 411 11 285 13.7 4.81 8.9 −5 874
002006 117 050 104 805 0 11 745 0 0 0 21.2 0 0 0
003001 72 358 67 109 0 1 028 1 728 −700 2 756 2.9 4.96 −2.0 −3 456
003002 62 884 57 738 0 2 386 3 792 −1 406 6 178 7.9 12.57 −4.7 −7 584
003003 290 063 270 823 0 12 098 9 623 2 475 21 721 8.6 6.86 1.8 −19 246
Fuente: Estimaciones del CONAPO.

Referencias

Librerias que se usaron en el documento

package loadedversion source
Cairo 1.6-1 CRAN (R 4.3.1)
chorddiag 0.1.3 Github ()
circlize 0.4.15 CRAN (R 4.3.1)
doMC 1.3.5 R-Forge (R 4.3.1)
dplyr 1.1.3 CRAN (R 4.3.2)
expss 0.11.6 CRAN (R 4.3.1)
foreach 1.5.2 CRAN (R 4.3.1)
ggalluvial 0.12.5 CRAN (R 4.3.1)
ggplot2 3.4.3 CRAN (R 4.3.1)
ggpubr 0.6.0 CRAN (R 4.3.1)
ggrepel 0.9.3 CRAN (R 4.3.1)
ggsankey 0.0.99999 Github ()
gt 0.10.0 CRAN (R 4.3.1)
haven 2.5.3 CRAN (R 4.3.1)
Hmisc 5.1-0 CRAN (R 4.3.1)
iterators 1.0.14 CRAN (R 4.3.1)
janitor 2.2.0 CRAN (R 4.3.1)
kableExtra 1.3.4 CRAN (R 4.3.1)
knitr 1.45 CRAN (R 4.3.2)
maditr 0.8.3 CRAN (R 4.3.1)
mapview 2.11.0 CRAN (R 4.3.1)
Matrix 1.6-1.1 CRAN (R 4.3.1)
network 1.18.1 CRAN (R 4.3.1)
openxlsx 4.2.5.2 CRAN (R 4.3.1)
reshape2 1.4.4 CRAN (R 4.3.1)
sjlabelled 1.2.0 CRAN (R 4.3.1)
sna 2.7-1 CRAN (R 4.3.1)
srvyr 1.2.0 CRAN (R 4.3.1)
statnet.common 4.9.0 CRAN (R 4.3.1)
stringr 1.5.0 CRAN (R 4.3.1)
survey 4.2 Github ()
survival 3.5-5 CRAN (R 4.3.1)
tibble 3.2.1 CRAN (R 4.3.1)
tidyr 1.3.0 CRAN (R 4.3.1)

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This work by Diana Villasana Ocampo is licensed under a Creative Commons Attribution 4.0 International License.